Reviewerly/exHarmony huggingface dataset:
Answering Multi-Dimensional Analytical Queries under Local Differential Privacy

Multi-dimensional analytical (MDA) queries are often issued against a fact table with predicates on (categorical or ordinal) dimensions and aggregations on one or more measures. In this paper, we study the problem of answering MDA queries under local differential privacy (LDP). In the absence of a trusted agent, sensitive dimensions are encoded in a privacy-preserving (LDP) way locally before being sent to the data collector. The data collector estimates the answers to MDA queries, based on the encoded dimensions. We propose several LDP encoders and estimation algorithms, to handle a large class of MDA queries with different types of predicates and aggregation functions. Our techniques are able to answer these queries with tight error bounds and scale well in high-dimensional settings (i.e., error is polylogarithmic in dimension sizes). We conduct experiments on real and synthetic data to verify our theoretical results, and compare our solution with marginal-estimation based solutions.
Practical Annealing-Based Quantum Computing

An overview of quantum computers based on the annealing paradigm and manufactured by D-Wave is given. An introductory survey of this approach to quantum computing (QC), together with a snapshot of what is known about performance, is presented. Evidence-based predictions about developments in this region of the QC space are presented.
Co-designing with people with dementia: A scoping review of involving people with dementia in design research

Co-designing with people with dementia (PwD) can uncover their needs and preferences, which have been often overlooked. It is difficult for PwD to understand designers and express themselves in a conventional co-design session. This study aims to evaluate the effects of involving PwD in design research on both PwD and the design process; to identify the trends of involving PwD in design research; to extract tools, recommendations, and limitations of involving PwD from reviewed studies to update the recommendations on how to co-design with PwD. A scoping review was carried out within the electronic databases PubMed and Scopus, and eight research questions were proposed, in order to gain specific knowledge on the involvement of PwD in design research. Twenty-six studies met the inclusion criteria, and 32 sessions were evaluated. Beneficial effects on both PwD and the design process were reported. The number of studies involving PwD in the moderate and severe stages of dementia has increased. Based on the review, an update of the existing tools and recommendations for co-designing with PwD is provided and a list of limitations of involving PwD is presented. The review shows that involving PwD in design research is beneficial for both the PwD and the design process, and there is a shift towards involving people who are in the moderate and severe stages of dementia. The authors propose that multidisciplinary meetings and case studies should be carried out to evaluate and refine the list of tools and recommendations as well as the list of limitations generated in this review.
AKUPM

Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. However, when incorporating entities from a knowledge graph to represent users, most existing works are unaware of the relationships between these entities and users. As a result, the recommendation results may suffer a lot from some unrelated entities.
Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects' activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject.
Improving Short Answer Grading Using Transformer-Based Pre-training

Dialogue-based tutoring platforms have shown great promise in helping individual students improve mastery. Short answer grading is a crucial component of such platforms. However, generative short answer grading using the same platform for diverse disciplines and titles is a crucial challenge due to data distribution variations across domains and a frequent occurrence of non-sentential answers. Recent NLP research has introduced novel deep learning architectures such as the Transformer, which merely uses self-attention mechanisms. Pre-trained models based on the Transformer architecture have been used to produce impressive results across a range of NLP tasks. In this work, we experiment with fine-tuning a pre-trained self-attention language model, namely Bidirectional Encoder Representations from Transformers (BERT) applying it to short answer grading, and show that it produces superior results across multiple domains. On the benchmarking dataset of SemEval-2013, we report up to 10% absolute improvement in macro-average-F1 over state-of-the-art results. On our two psychology domain datasets, the fine-tuned model yields classification almost up to the human-agreement levels. Moreover, we study the effectiveness of fine-tuning as a function of the size of the task-specific labeled data, the number of training epochs, and its generalizability to cross-domain and join-domain scenarios.
Review on “Blockchain technology based medical healthcare system with privacy issues”

Healthcare has been the industry with the highest boom in terms of both revenue and data. With so much of electronic health records, the security has been the need of the hour. To make this critical information more secure, there has been an urge to use the blockchain technology. Therefore, researches came up with a solution of blockchain technology in medical healthcare that will not only protect data from being tampered but will also ensure that the data leakage is stopped. This technology could preserve data and thus guarantee reliability. And if this technology is used along with cloud computing technology, problems related to storage can also be vanished, because cloud is trusted for storing and managing data. Also, the blockchain can address the security issues of the cloud. Indeed, medical data sharing and storing with Blockchain‐based cloud can address a lot of issues of medical data. The main aim of this paper is to present the current state‐of‐the‐art on blockchain‐based medical healthcare system. We have clearly discussed several existing works on the same domain and also presented a comparative study among the published works.
Physically Secure Lightweight Anonymous User Authentication Protocol for Internet of Things Using Physically Unclonable Functions

The Internet of Things (IoT) acts as an umbrella for the Internet-enabled devices for various applications, such as smart home, smart city, smart grid, and smart healthcare. The emergence of the immense economic potential necessitates a robust authentication mechanism that needs to be lightweight and suitable for real-time applications. Moreover, the physical integrity of these devices cannot be assumed as these are designed to be deployed in an unattended environment with minimum human supervision. A user authentication mechanism for the IoT, in addition to guaranteeing user anonymity and un-traceability functionality requirements, must also be resistant to device physical capture and related misuses. In this paper, we present a novel lightweight anonymous user authentication protocol for the IoT environment by utilizing “cryptographic one-way hash function”, “physically unclonable function (PUF)” and “bitwise exclusive-OR (XOR)” operations. The broadly accepted Real-Or-Random (ROR) model-based formal security analysis, formal security verification using the automated software verification tool, namely “automated validation of internet security protocols and applications (AVISPA)” and also non-mathematical (informal) security analysis have been carried out on the proposed scheme. It is shown that the proposed scheme has the ability to resist various well-known attacks that are crucial for securing the IoT environment. Through a detailed comparative study, we show that the proposed scheme outperforms other existing related schemes in terms of computation and communication costs, and also security & functionality features. Finally, a practical demonstration of the proposed scheme using the NS3 simulation has been provided for measuring various network performance parameters.
Binary optimization by momentum annealing

One of the vital roles of computing is to solve large-scale combinatorial optimization problems in a short time. In recent years, methods have been proposed that map optimization problems to ones of searching for the ground state of an Ising model by using a stochastic process. Simulated annealing (SA) is a representative algorithm. However, it is inherently difficult to perform a parallel search. Here we propose an algorithm called momentum annealing (MA), which, unlike SA, updates all spins of fully connected Ising models simultaneously and can be implemented on GPUs that are widely used for scientific computing. MA running in parallel on GPUs is 250 times faster than SA running on a modern CPU at solving problems involving 100 000 spin Ising models.
Video Activity Recognition: State-of-the-Art

Video activity recognition, although being an emerging task, has been the subject of important research efforts due to the importance of its everyday applications. Surveillance by video cameras could benefit greatly by advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. The aim of this paper is to survey the state-of-the-art techniques for video activity recognition while at the same time mentioning other techniques used for the same task that the research community has known for several years. For each of the analyzed methods, its contribution over previous works and the proposed approach performance are discussed.
Fair Transfer Learning with Missing Protected Attributes

Risk assessment is a growing use for machine learning models. When used in high-stakes applications, especially ones regulated by anti-discrimination laws or governed by societal norms for fairness, it is important to ensure that learned models do not propagate and scale any biases that may exist in training data. In this paper, we add on an additional challenge beyond fairness: unsupervised domain adaptation to covariate shift between a source and target distribution. Motivated by the real-world problem of risk assessment in new markets for health insurance in the United States and mobile money-based loans in East Africa, we provide a precise formulation of the machine learning with covariate shift and score parity problem. Our formulation focuses on situations in which protected attributes are not available in either the source or target domain. We propose two new weighting methods: prevalence-constrained covariate shift (PCCS) which does not require protected attributes in the target domain and target-fair covariate shift (TFCS) which does not require protected attributes in the source domain. We empirically demonstrate their efficacy in two applications.
Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker

Object tracking is a hot topic in computer vision. Thanks to the booming of the very high resolution (VHR) remote sensing techniques, it is now possible to track targets of interests in satellite videos. However, since the targets in the satellite videos are usually too small in comparison with the entire image, and too similar with the background, most state-of-the-art algorithms failed to track the target in satellite videos with a satisfactory accuracy. Due to the fact that optical flow shows great potential to detect even the slight movement of the targets, we proposed a multiframe optical flow tracker for object tracking in satellite videos. The Lucas-Kanade optical flow method was fused with the HSV color system and integral image to track the targets in the satellite videos, while multiframe difference method was utilized in the optical flow tracker for a better interpretation. The experiments with five VHR remote sensing satellite video datasets indicate that compared with state-of-the-art object tracking algorithms, the proposed method can track the target more accurately.
Unsupervised object-level video summarization with online motion auto-encoder

Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i.e., objects of interest and their key motions) in online videos has been barely touched. In this paper, we investigate a pioneer research direction towards the fine-grained unsupervised object-level video summarization. It can be distinguished from existing pipelines in two aspects: extracting key motions of participated objects, and learning to summarize in an unsupervised and online manner. To achieve this goal, we propose a novel online motion Auto-Encoder (online motion-AE) framework that functions on the super-segmented object motion clips. Comprehensive experiments on a newly-collected surveillance dataset and public datasets have demonstrated the effectiveness of our proposed method.
Face-from-Depth for Head Pose Estimation on Depth Images

Depth cameras allow to set up reliable solutions for people monitoring and behavior understanding, especially when unstable or poor illumination conditions make unusable common RGB sensors. Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only. A head detection and localization module is also included, in order to develop a complete end-to-end system. The core element of the framework is a Convolutional Neural Network, called POSEidon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , that receives as input three types of images and provides the 3D angles of the pose as output. Moreover, a Face-from-Depth component based on a Deterministic Conditional GAN model is able to hallucinate a face from the corresponding depth image. We empirically demonstrate that this positively impacts the system performances. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Experimental results show that our method overcomes several recent state-of-art works based on both intensity and depth input data, running in real-time at more than 30 frames per second.
Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions

<h3>Importance</h3> Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools. <h3>Objective</h3> To develop and evaluate computer vision methods to assist pathologists in diagnosing the full spectrum of breast biopsy samples, from benign to invasive cancer. <h3>Design, Setting, and Participants</h3> In this diagnostic study, 240 breast biopsies from Breast Cancer Surveillance Consortium registries that varied by breast density, diagnosis, patient age, and biopsy type were selected, reviewed, and categorized by 3 expert pathologists as benign, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. The atypia and DCIS cases were oversampled to increase statistical power. High-resolution digital slide images were obtained, and 2 automated image features (tissue distribution feature and structure feature) were developed and evaluated according to the consensus diagnosis of the expert panel. The performance of the automated image analysis methods was compared with independent interpretations from 87 practicing US pathologists. Data analysis was performed between February 2017 and February 2019. <h3>Main Outcomes and Measures</h3> Diagnostic accuracy defined by consensus reference standard of 3 experienced breast pathologists. <h3>Results</h3> The accuracy of machine learning tissue distribution features, structure features, and pathologists for classification of invasive cancer vs noninvasive cancer was 0.94, 0.91, and 0.98, respectively; the accuracy of classification of atypia and DCIS vs benign tissue was 0.70, 0.70, and 0.81, respectively; and the accuracy of classification of DCIS vs atypia was 0.83, 0.85, and 0.80, respectively. The sensitivity of both machine learning features was lower than that of the pathologists for the invasive vs noninvasive classification (tissue distribution feature, 0.70; structure feature, 0.49; pathologists, 0.84) but higher for the classification of atypia and DCIS vs benign cases (tissue distribution feature, 0.79; structure feature, 0.85; pathologists, 0.72) and the classification of DCIS vs atypia (tissue distribution feature, 0.88; structure feature, 0.89; pathologists, 0.70). For the DCIS vs atypia classification, the specificity of the machine learning feature classification was similar to that of the pathologists (tissue distribution feature, 0.78; structure feature, 0.80; pathologists, 0.82). <h3>Conclusion and Relevance</h3> The computer-based automated approach to interpreting breast pathology showed promise, especially as a diagnostic aid in differentiating DCIS from atypical hyperplasia.
Revolution or Evolution? Speech Interaction and HCI Design Guidelines

The evolution of designing interactive interfaces has been rather incremental over the past few decades, largely focused on graphical user interfaces (GUIs), even as these extended from the desktop, to mobile or to wearables. Only recently can we engage in ubiquitous, ambient, and seamless interactions, as afforded by voice user interfaces (VUIs) such as smart speakers. We posit here that recent speech engineering advances present an opportunity to revolutionize the design of voice interactions. Yet current design guidelines or heuristics are heavily oriented towards GUI interaction, and thus may not fully facilitate the design of VUIs. We survey current research revealing the challenges of applying GUI design principles to this space, as well as critique efforts to develop VUI-specific heuristics. We use these to argue that the path toward revolutionary new ubiquitous conversational voice interactions must be based on several evolutionary steps that build VUI heuristics off existing GUI design principles.
Characterizing Asymmetric Collaborative Interactions in Virtual and Augmented Realities

We present an assessment of asymmetric interactions in Collaborative Virtual Environments (CVEs). In our asymmetric setup, two co-located users interact with virtual 3D objects, one in immersive Virtual Reality (VR) and the other in mobile Augmented Reality (AR). We conducted a study with 36 participants to evaluate performance and collaboration aspects of pair work, and compare it with two symmetric scenarios, either with both users in immersive VR or mobile AR. To perform this experiment, we adopt a collaborative AR manipulation technique from literature and develop and evaluate a VR manipulation technique of our own. Our results indicate that pairs in asymmetric VR-AR achieved significantly better performance than the AR symmetric condition, and similar performance to VR symmetric. Regardless of the condition, pairs had similar work participation indicating a high cooperation level even when there is a visualization and interaction asymmetry between the participants.
Subvector Commitments with Application to Succinct Arguments

We put forward the notion of subvector commitments (SVC): An SVC allows one to open a committed vector at a set of positions, where the opening size is independent of length of the committed vector and the number of positions to be opened. We propose two constructions under variants of the root assumption and the CDH assumption, respectively. We further generalize SVC to a notion called linear map commitments (LMC), which allows one to open a committed vector to its images under linear maps with a single short message, and propose a construction over pairing groups. Equipped with these newly developed tools, we revisit the “CS proofs” paradigm [Micali, FOCS 1994] which turns any arguments with public-coin verifiers into non-interactive arguments using the Fiat-Shamir transform in the random oracle model. We propose a compiler that turns any (linear, resp.) PCP into a non-interactive argument, using exclusively SVCs (LMCs, resp.). For an approximate 80 bits of soundness, we highlight the following new implications:
Deanonymization and Linkability of Cryptocurrency Transactions Based on Network Analysis

Bitcoin, introduced in 2008 and launched in 2009, is the first digital currency to solve the double spending problem without relying on a trusted third party.Bitcoin provides a way to transact without any trusted intermediary, but its privacy guarantees are questionable.Despite the fact that Bitcoin addresses are not linked to any identity, multiple deanonymization attacks have been proposed.Alternative cryptocurrencies such as Dash, Monero, and Zcash aim to provide stronger privacy by using sophisticated cryptographic techniques to obfuscate transaction data.Previous work in cryptocurrency privacy mostly focused on applying data mining algorithms to the transaction graph extracted from the blockchain.We focus on a less well researched vector for privacy attacks: network analysis.We argue that timings of transaction messages leak information about their origin, which can be exploited by a well connected adversarial node.For the first time, network level attacks on Bitcoin and the three major privacy-focused cryptocurrencies have been examined.We describe the message propagation mechanics and privacy guarantees in Bitcoin, Dash, Monero, and Zcash.We propose a novel technique for linking transactions based on transaction propagation analysis.We also unpack address advertisement messages (ADDR), which under certain assumptions may help in linking transaction clusters to IP addresses of nodes.We implement and evaluate our method, deanonymizing our own transactions in Bitcoin and Zcash with a high level of accuracy.We also show that our technique is applicable to Dash and Monero.We estimate the cost of a full-scale attack on the Bitcoin mainnet at hundreds of US dollars, feasible even for a low budget adversary.