Reviewerly/exHarmony huggingface dataset:
Continuance Use of Cloud Computing in Higher Education Institutions: A Conceptual Model

Resource optimization is a key concern for Higher Education Institutions (HEIs). Cloud Computing, as the recent generation in computing technology of the fourth industrial revolution, has emerged as the main standard of service and resource delivery. As cloud computing has grown into a mature technology and is being rapidly adopted in many HEIs across the world, retaining customers of this innovative technology has become a challenge to the cloud service providers. Current research trends on cloud computing have sought to study the acceptance or adoption of technology; however, little research has been devoted to the continuance use in an organizational setting. To address this gap, this study aims to investigate the antecedents of cloud computing continuance use in HEIs. Hence, drawing on the prior literature in organizational-level continuance, this research established a conceptual model that extends and contextualizes the IS continuance model through the lens of the TOE framework (i.e., technological, organizational, and environmental influences). The results of a pilot study, conducted through a survey with information and communications technology (ICT) decision makers, and based on the proposed conceptual model, indicate that the instrument is both reliable and valid, and so point the way towards further research. The paper closes with a discussion of the research limitations, contribution, and future directions.
In-Home Floor Based Sensor System-Smart Carpet- to Facilitate Healthy Aging in Place (AIP)

With the rapidly aging population, there is a need to detect elderly's activity, monitor their health, alert health care personnel, and provide real-time and long-term access to the generated sensor data.With the advances in sensor technologies, communication protocols, computing power, and cloud and edge services, it is now possible to build smart assistive living systems to improve people's lives.In this research, we present a Context-aware and private real-time reporting aging in place system.The proposed system, we call it smart carpet, consists of a sensor pad placed under a carpet; the electronics reads walking activity to provide an automated health monitoring and alert system.We extended the system's functionalities to improve its ability to detect falls, measure gait, and count the number of people traversing the carpet (socializing).In an urgent and time-sensitive situation, we need to provide a real-time notification.We propose a cooperative cloudlet model, where the sensors' data will be sent to the nearest Cloudlet for analysis and extracting real-time decisions in minimal delay.Results showed that our system could assist the elderly in detecting falls with 95% sensitivity and 85% specificity.Measuring and estimating their gait with a mean percentage error difference to GAITRite 1.43% in walking speed; hence, predicting a fall risk and counting people's plurality socializing with the elderly with an average accuracy of 100%.We evaluated our system's improvements in a controlled environment.We are looking forward to deploying the system in a nursing home (after the COVID -19 is over) for more data gathering and validations.
Attention Based Multiple Instance Learning for Classification of Blood Cell Disorders

Red blood cells are highly deformable and present in various shapes. In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis. However, manually labeling of all cells is laborious, complicated and introduces inter-expert variability. We propose an attention based multiple instance learning method to classify blood samples of patients suffering from blood cell disorders. Cells are detected using an R-CNN architecture. With the features extracted for each cell, a multiple instance learning method classifies patient samples into one out of four blood cell disorders. The attention mechanism provides a measure of the contribution of each cell to the overall classification and significantly improves the networks classification accuracy as well as its interpretability for the medical expert.
Towards Natural Bilingual and Code-Switched Speech Synthesis Based on Mix of Monolingual Recordings and Cross-Lingual Voice Conversion

Recent state-of-the-art neural text-to-speech (TTS) synthesis models have dramatically improved intelligibility and naturalness of generated speech from text. However, building a good bilingual or code-switched TTS for a particular voice is still a challenge. The main reason is that it is not easy to obtain a bilingual corpus from a speaker who achieves native-level fluency in both languages. In this paper, we explore the use of Mandarin speech recordings from a Mandarin speaker, and English speech recordings from another English speaker to build high-quality bilingual and code-switched TTS for both speakers. A Tacotron2-based cross-lingual voice conversion system is employed to generate the Mandarin speaker's English speech and the English speaker's Mandarin speech, which show good naturalness and speaker similarity. The obtained bilingual data are then augmented with code-switched utterances synthesized using a Transformer model. With these data, three neural TTS models -- Tacotron2, Transformer and FastSpeech are applied for building bilingual and code-switched TTS. Subjective evaluation results show that all the three systems can produce (near-)native-level speech in both languages for each of the speaker.
Compositional Explanations of Neurons

We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts that closely approximate neuron behavior. Compared to prior work that uses atomic labels as explanations, analyzing neurons compositionally allows us to more precisely and expressively characterize their behavior. We use this procedure to answer several questions on interpretability in models for vision and natural language processing. First, we examine the kinds of abstractions learned by neurons. In image classification, we find that many neurons learn highly abstract but semantically coherent visual concepts, while other polysemantic neurons detect multiple unrelated features; in natural language inference (NLI), neurons learn shallow lexical heuristics from dataset biases. Second, we see whether compositional explanations give us insight into model performance: vision neurons that detect human-interpretable concepts are positively correlated with task performance, while NLI neurons that fire for shallow heuristics are negatively correlated with task performance. Finally, we show how compositional explanations provide an accessible way for end users to produce simple copy-paste adversarial examples that change model behavior in predictable ways.
A1: A Distributed In-Memory Graph Database

A1 is an in-memory distributed database used by the Bing search engine to support complex queries over structured data. The key enablers for A1 are availability of cheap DRAM and high speed RDMA (Remote Direct Memory Access) networking in commodity hardware. A1 uses FaRM [11,12] as its underlying storage layer and builds the graph abstraction and query engine on top. The combination of in-memory storage and RDMA access requires rethinking how data is allocated, organized and queried in a large distributed system. A single A1 cluster can store tens of billions of vertices and edges and support a throughput of 350+ million of vertex reads per second with end to end query latency in single digit milliseconds. In this paper we describe the A1 data model, RDMA optimized data structures and query execution.
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?

A learning algorithm is private if the produced model does not reveal (too much) about its training set. InstaHide [Huang, Song, Li, Arora, ICML'20] is a recent proposal that claims to preserve privacy by an encoding mechanism that modifies the inputs before being processed by the normal learner. We present a reconstruction attack on InstaHide that is able to use the encoded images to recover visually recognizable versions of the original images. Our attack is effective and efficient, and empirically breaks InstaHide on CIFAR-10, CIFAR-100, and the recently released InstaHide Challenge. We further formalize various privacy notions of learning through instance encoding and investigate the possibility of achieving these notions. We prove barriers against achieving (indistinguishability based notions of) privacy through any learning protocol that uses instance encoding.
A survey of typical attributed graph queries

Graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. Attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose a taxonomy of attributed graph queries based on query inputs and outputs. We summarize the definitions of queries that fall into each category and present a fine-grained classification of queries within each category by analyzing the semantics and algorithmic motivations behind these queries. Moreover, we discuss the insights of how existing studies address the technical challenges of query processing and outline several promising future research directions.
Classical zero-knowledge arguments for quantum computations

We show that every language in QMA admits a classical-verifier, quantum-prover zero-knowledge argument system which is sound against quantum polynomial-time provers and zero-knowledge for classical (and quantum) polynomial-time verifiers. The protocol builds upon two recent results: a computational zero-knowledge proof system for languages in QMA, with a quantum verifier, introduced by Broadbent et al. (FOCS 2016), and an argument system for languages in QMA, with a classical verifier, introduced by Mahadev (FOCS 2018).
FHDe2Net: Full High Definition Demoireing Network

Frequency aliasing in the digital capture of display screens leads to the moiré pattern, appearing as stripe-shaped distortions in images. Efforts to demoiréing have been made recently in a learning fashion due to the complexity and diversity of the pattern appearance. However, existing methods cannot satisfy the practical demand of demoiréing on camera phone capturing more pixels than a full high definition (FHD) image, which poses additional challenges of wider pattern scale range and fine detail preservation. We propose the Full High Definition Demoiréing Network (FHDe $$^{2}$$ Net) to solve such problems. The framework consists of a global to local cascaded removal branch to eradicate multi-scale moiré patterns and a frequency based high-resolution content separation branch to retain fine details. We further collect an FHD moiré image dataset as a new benchmark for training and evaluation. Comparison experiments and ablation studies have verified the effectiveness of the proposed framework and each functional module both quantitatively and qualitatively in practical application scenarios.
Design Space for Graph Neural Networks

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function. Additionally, GNN designs are often specialized to a single task, yet few efforts have been made to understand how to quickly find the best GNN design for a novel task or a novel dataset. Here we define and systematically study the architectural design space for GNNs which consists of 315,000 different designs over 32 different predictive tasks. Our approach features three key innovations: (1) A general GNN design space; (2) a GNN task space with a similarity metric, so that for a given novel task/dataset, we can quickly identify/transfer the best performing architecture; (3) an efficient and effective design space evaluation method which allows insights to be distilled from a huge number of model-task combinations. Our key results include: (1) A comprehensive set of guidelines for designing well-performing GNNs; (2) while best GNN designs for different tasks vary significantly, the GNN task space allows for transferring the best designs across different tasks; (3) models discovered using our design space achieve state-of-the-art performance. Overall, our work offers a principled and scalable approach to transition from studying individual GNN designs for specific tasks, to systematically studying the GNN design space and the task space. Finally, we release GraphGym, a powerful platform for exploring different GNN designs and tasks. GraphGym features modularized GNN implementation, standardized GNN evaluation, and reproducible and scalable experiment management.
Exploration Enhanced RPSO for Collaborative Multitarget Searching of Robotic Swarms

Particle Swarm Optimization (PSO) is an excellent population-based optimization algorithm. Meanwhile, because of its inspiration source and the velocity update feature, it is also widely used in the collaborative searching tasks for swarm robotics. One of the PSO-based models for robotic swarm searching tasks is Robotic PSO (RPSO). It adds additional items for obstacle avoidance into standard PSO and has been applied to many single-target search tasks. However, due to PSO’s global optimization characteristics, it is easy to converge to a specific position in the search space and lose the ability to explore further. When faced with the problem of multitarget searching, it may become inefficient or even invalid. This paper proposes an Exploration Enhanced Robotic PSO (E2RPSO) method for multitarget searching problems for robotic swarms. The proposed method modifies the third item in the RPSO as an additional attraction term. This item not only enables the robot to avoid collisions but also guides the swarm to search unexplored regions as much as possible. This operation increases the swarm’s task-specific (top-down) diversity, making the system cover a broader search area and avoid falling into local optimums. Besides, the aggregation degree and evolution speed factors are also included in determining the inertia weight of the proposed method, which adjusts the swarm’s internal (bottom-up) diversity dynamically. The comparison results show that this method can balance the relationship between exploration and exploitation well, which has the potential to be applied to multitarget searching scenarios.
Behavior regularized prototypical networks for semi-supervised few-shot image classification

We propose a Behavior Regularized Prototypical Network (BR-ProtoNet) for few-shot image classification in semi-supervised scenarios. To learn a generalizable metric, we exploit readily-available unlabeled data and construct complementary constraints to regularize the model’s behavior. Specifically, we match the label spaces between each episode and the whole training set. The predictions on the unlabeled data over different episodes can be aggregated to capture more reliable category information. We further construct new instances via adversarial perturbation and interpolation. These instances regularize the model’s behavior over the neighborhoods of the original ones and along the interpolation paths among them. In addition, they ensure the learnt embedding space possesses the property of proximity preservation. The regularization of these aspects is incorporated into the optimization process of BR-ProtoNet on partially labeled data. We have conducted thorough experiments on multiple challenging benchmarks. The results suggest that the metric learning can significantly benefit from the proposed regularization, and thus leading to the state-of-the-art performance in semi-supervised few-shot image classification.
An optimized robust watermarking technique using CKGSA in frequency domain

The existing SVD based watermarking techniques insert watermark into the singular component which causes the false positive problem (FPP). To overcome this problem, a robust watermarking technique using a chaotic kbest gravitational search algorithm (CKGSA) in DCT and SVD domain is proposed. To eliminate the FPP, the watermark is embedded into the principal component (PC) of the cover image rather than the singular value. The objective to use CKGSA is to optimize the value of multiple embedding factors so that robustness and imperceptibility can be balanced. Arnold transform (AT) is applied to the watermark image before embedding, to enhance security and robustness. The performance of the proposed technique has been experimented on a standard dataset and compared with the six recent state-of-the-art techniques including DS-DE, DS-PSO, DCT-SVD, DDS-PSO, DS-GSA, and DS-TLBO in terms of imperceptibility (PSNR, SSIM) and robustness (PSNR, NC). The experimental results demonstrate that the proposed technique outperforms the other existing techniques.
Resource Allocation and Consensus of Blockchains in Pervasive Edge Computing Environments

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage and propose a recent block storage allocation scheme for quick retrieval of missing blocks. We develop data migration algorithms to dynamically reallocate data and block storage to adapt topology changes in the network. The proposed blockchain system can also reach consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 18.4 percent less time and consumes 87 percent less battery power when compared with traditional blockchain systems.
Structured Information Extraction of Pathology Reports with Attention-based Graph Convolutional Network

Electronic medical data contains biochemical, imaging, pathological information during diagnosis and treatment. The pathology report is a kind of highly liberalized unstructured textual data, which is the basis and gold standard of cancer diagnosis and is very important for the prognosis and treatment of patients. The application of information extraction technology to pathological reports can obtain structured data that can be understood and analyzed by computers, helping pathologists make appropriate decisions. In this work, we proposed an attention-based graph convolutional network (GCN) for converting unstructured pathological reports into a structured form suitable for computer analysis to improve the current pathologist's workflow, collected medical data from different platforms, and provided more accurate assistance for diagnosis and treatment. We used pathology reports data from TCGA (The Cancer Genome Atlas) database with fine-grained annotations on 3632 pathology reports including four types of cancers. Our method performs better in our pathology report dataset with higher F1 score than traditional methods and deep learning methods. The results indicate that our method is robust, thus may work with other types of cancer pathology report.
Classifying Promotion Images Using Optical Character Recognition and Naïve Bayes Classifier

Promotion is one of the most effective ways to promote a business, and most people love promotions. Usually these businesses announce their promo by uploading images to social medias such as Instagram. However, most of the time these promo images are buried in the sea of other non-promotional images. It would be more practical if computers could be utilized to automatically look for images containing promotional offers. That is why this research is done to discuss about creating a system that is able to tell whether an image contains information about a promotional offer or not automatically without human intervention using Optical Character Recognition (OCR) and Naïve Bayes Algorithm as the classifier. Random Forest and K-Nearest Neighbor are also used as a comparison to the Naïve Bayes Algorithm. In this experiment we use cross validation method where we divide 158 images into five groups to train and test our model. The Naïve Bayes model achieved 94,31% accuracy, 94,33 % recall, 94,11 % precision, and 0.93 F1 score on average, which is the highest among these three algorithms. Based on the results, we can conclude that Optical Character Recognition (OCR) and Naïve Bayes Algorithm are quite suitable for this problem.
Opportunities for Reference Services after Covid-19

There is something to be learned from how academic library reference services adapted to providing accurate and timely information to students, faculty, staff, and other users during a global pandemic. In this spirit I present, in increasing order of importance, three specific opportunities for reference services in a post Covid-19 world: evolution in technology, distributed staffing models, and measuring efficacy against student learning and success. Most importantly, reference services ought to adapt its core mission to one focused on student learning and success given the likely financial realities academia and academic libraries will face in a post Covid-19 environment.
Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance

Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.