Reviewerly/exHarmony huggingface dataset:
Practical and Secure Outsourcing Algorithms of Matrix Operations Based on a Novel Matrix Encryption Method

With the recent growth and commercialization of cloud computing, outsourcing computation has become one of the most important cloud services, which allows the resource-constrained clients to efficiently perform large-scale computation in a pay-per-use manner.Meanwhile, outsourcing large scale computing problems and computationally intensive applications to the cloud has become prevalent in the science and engineering computing community.As important fundamental operations, large-scale matrix multiplication computation (MMC), matrix inversion computation (MIC), and matrix determinant computation (MDC) have been frequently used.In this paper, we present three new algorithms to enable secure, verifiable, and efficient outsourcing of MMC, MIC, and MDC operations to a cloud that may be potentially malicious.The main idea behind our algorithms is a novel matrix encryption/decryption method utilizing consecutive and sparse unimodular matrix transformations.Compared to previous works, this versatile technique can be applied to many matrix operations while achieving a good balance between security and efficiency.First, the proposed algorithms provide robust confidentiality by concealing the local information of the entries in the input matrices.Besides, they also protect the statistic information of the original matrix.Moreover, these algorithms are highly efficient.Our theoretical analysis indicates that the proposed algorithms reduce the time overhead on the client side from O(n 2.3728639 ) to O(n 2 ).Finally, the extensive experimental evaluations demonstrate the practical efficiency and effectiveness of our algorithms.
JigFab

We present JigFab, an integrated end-to-end system that supports casual makers in designing and fabricating constructions with power tools. Starting from a digital version of the construction, JigFab achieves this by generating various types of constraints that configure and physically aid the movement of a power tool. Constraints are generated for every operation and are custom to the work piece. Constraints are laser cut and assembled together with predefined parts to reduce waste. JigFab's constraints are used according to an interactive step-by-step manual. JigFab internalizes all the required domain knowledge for designing and building intricate structures, consisting of various types of finger joints, tenon & mortise joints, grooves, and dowels. Building such structures is normally reserved for artisans or automated with advanced CNC machinery.
3D Pen + 3D Printer

The emergence of a 3D pen brings 3D modeling from a screen-based computer-aided design (CAD) system and 3D printing to direct and rapid crafting by 3D doodling. However, 3D doodling remains challenging, requiring craft skills to rapidly express an idea, which is critical in creative making. We explore a new process of 3D modeling using 3D pen + 3D printer. Our pilot study shows that users need support to reduce the number of non-creative tasks to explore a wide design strategy. With the opportunity to invent a new 3D modeling process that needs to incorporate both a pen and printer, we propose techniques and a system that empower users to print while doodling to focus on creative exploration. Our user study shows that users can create diverse 3D models using a pen and printer. We discuss the roles of the human and fabrication machine for the future of fabrication.
Beyond Birthday Bound Secure MAC in Faulty Nonce Model

Encrypt-then-MAC (EtM) is a popular mode for authenticated encryption (AE). Unfortunately, almost all designs following the EtM paradigm, including the AE suites for TLS, are vulnerable against nonce misuse. A single repetition of the nonce value reveals the hash key, leading to a universal forgery attack. There are only two authenticated encryption schemes following the EtM paradigm which can resist nonce misuse attacks, the GCM-RUP (CRYPTO-17) and the $$\mathsf {GCM/2}^{+} $$ (INSCRYPT-12). However, they are secure only up to the birthday bound in the nonce respecting setting, resulting in a restriction on the data limit for a single key. In this paper we show that nEHtM, a nonce-based variant of EHtM (FSE-10) constructed using a block cipher, has a beyond birthday bound (BBB) unforgeable security that gracefully degrades under nonce misuse. We combine nEHtM with the CENC (FSE-06) mode of encryption using the EtM paradigm to realize a nonce-based AE, CWC+. CWC+ is very close (requiring only a few more xor operations) to the CWC AE scheme (FSE-04) and it not only provides BBB security but also gracefully degrading security on nonce misuse.
User preferences prediction approach based on embedded deep summaries

Some existing preference prediction methods have utilized users' review texts to learn additional knowledge to support the prediction task. Such methods determine and represent users' preference knowledge by conducting user sentiment, aspect sentiment and topic analysis as recognized in the review texts. However, the discovered item topics from topic-based methods may not fit the preferences of most users while the discovered users' opinions and aspects' sentiments from sentiment based methods may not reflect each user's opinion. This paper proposes a hybrid approach to learn and represent users' preference knowledge from review texts and utilize the acquired representation to support rating prediction. Our approach assumes that user preferences are affected by relevant item aspects and majority preference which can be captured through proper summarization and representation of users' review texts. Thus, two deep learning practices are established: the recurrent neural network - Long Short-Term Memory (RNN-LSTM) architecture to learn users' preference knowledge along with item aspects which influence preferences and the Doc2Vec algorithm to convert the acquired knowledge to a suitable representation. The approach extends probabilistic matrix factorization (PMF) model by strengthening its latent factors predictions with the acquired preference knowledge which is used to regulate the predictions. Our experiments on the Amazon products dataset have revealed the capability of learning a suitable representation for users' preference knowledge and its impact on rating prediction as our proposed approach beats alternative methods.
Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images

In medical image analysis for disease diagnosis, segmentation is one of the challenging tasks. Owing to the inherited degradations in MRI improper segments are produced. Segmentation process is an important step in brain tissue analysis. Moreover, an early detection of plaque in carotid artery using ultrasound images may prevent serious brain strokes. Unfortunately, low quality and noisy ultrasound images are still challenges for accurate segmentation. The objective of this research is to develop a robust segmentation approach for medical images such as brain MRI and carotid artery ultrasound images.In this paper, a novel approach is proposed to address the segmentation challenges of medical images. The proposed approach employed fuzzy intelligence and Gaussian mixture model (GMM). It comprises two phases; firstly, incorporating spatial fuzzy c-means in GMM by exploiting statistical, texture, and wavelet image features. During model development, GMM parameters are estimated in presence of noise by EM algorithm iteratively. Utilizing these parameters, brain MRI images are segmented. In next phase, developed approach is applied to solve a real problem of carotid artery plaque detection using ultrasound images. The dataset of real patients annotated by radiologists has been obtained from Radiology Department, Shifa International Hospital Islamabad, Pakistan. For this, intima-media-thickness values are computed from the proposed segmentation followed by support vector machines for plaque classification (normal/abnormal).The obtained segmentation has been evaluated on standard brain MRI dataset and offers high segmentation accuracy of 99.2%. The proposed approach outperforms in term of segmentation performance range of 3-9% as compared to the state of the art approaches on brain MRI. Furthermore, the proposed approach shows robustness to various levels of Gaussian and Rician image noises. On carotid artery dataset, we have obtained high plaque detection rate in terms of accuracy, sensitivity, specificity, and F-score values of 98.8%, 99.3%, 98.0%, and 97.5% respectively.The proposed approach segments both modalities with high precision and shows robustness at Gaussian and Rician noise levels. Results for brain MRI and ultrasound images indicate its effectiveness and can be used as second opinion in addition to the radiologists. The developed approach is straightforward, efficient, and reproducible. It may benefit to improve the clinical evaluation of the disease in both asymptomatic and symptomatic individuals.
The art of note taking with mobile devices in medical education

Students use mobile devices extensively in their everyday life, and the new technology is adopted in study usage. Since 2013, the University of Helsinki has given new medical and dental students iPads for study use. Simultaneously, an action research project on mobile learning started focusing on these students' mobile device usage throughout their study years. Note taking is crucial in academic studies, but the research evidence in this area is scarce. The aims of this study were to explore medical and dental students' self-reported study uses of mobile devices and their best practices of mobile note taking.An action research project began in 2013 and followed the first student cohort (124 medical and 52 dental students) with iPads from the first until the fifth study year. We explored students' descriptions of their most important study uses of mobile devices and their perceptions of note taking with iPads. The longitudinal data were collected with online questionnaires over the years. The answers to open-ended questions were examined using qualitative content analysis. The findings were triangulated with another question on note taking and focus-group interviews.The response rates varied between 73 and 95%. Note taking was the most frequently and consistently reported study use of iPads during the study years. While taking notes, students processed the new information in an accomplished way and personalised the digital learning materials by making comments, underlining, marking images and drawing. The visual nature of their learning materials stimulated learning. Students organised the notes for retention in their personalised digital library. In the clinical studies, medical students faced the teachers' resistance and ambivalence to mobile device usage. This hindered the full-scale benefit of the novel technology in the clinical context.Efficient digital note taking practices were pivotal to students in becoming mobile learners. Having all their notes and learning materials organised in their personal digital libraries enabled the students to retrieve them anywhere, anytime, both when studying for examinations and treating patients in the clinical practice. The challenges the medical students met using mobile devices in the clinical setting require further studies.
Max-margin multi-scale convolutional factor analysis model with application to image classification

This paper presents a max-margin multi-scale convolutional factor analysis (MMCFA) model, which explores the strongly discriminative principle of max-margin learning to improve the classification performance of multi-scale convolutional factor analysis (CFA) model with application to image data classification. Compared with the traditional factor analysis (FA) model, the CFA model can maintain the spatial correlation among the image pixels in two-dimensional space and capture the structural information from images via convolution kernels. Moreover, to extract multi-level features, multi-scale convolution kernels are adopted to capture richer features at different scales of images. Since the unsupervised model may not offer discriminative factors for the classification task, it is expected to introduce the supervised information to the multi-scale CFA model when supervised information is available. To deal with it, a latent variable support vector machine (LVSVM) is linked to the factors learned from multi-scale CFA model, yielding max-margin discrimination, as the classification criterion in the feature space in our proposed model. The multi-scale CFA model and LVSVM learn parameters jointly in a united framework via the Gibbs inference. Experimental results on mixed national institute of standards and technology (MNIST) dataset, Fashion-MNIST dataset, SVHN dataset and measured synthetic aperture radar (SAR) images show that the learned convolution kernels and factors can describe data information well and the proposed model has excellent classification performance.
Contrast enhancement of Progressive Visual Secret Sharing (PVSS) scheme for gray-scale and color images using super-resolution

Traditional Visual Secret Sharing (VSS) scheme encrypts the secret image into multiple shares. It recovers the secret image based on the "all or nothing" methodology (i.e. all shares must be stacked together to recover the secret image else nothing will be revealed). The modern VSS schemes differ from traditional VSS by progressively recovering the secret image by stacking number of shares. These are also referred as Progressive VSS (PVSS). PVSS schemes generate two types of shares namely 1) meaningful (are the shares which have a meaningful image embedded as the cover image on top of the shares) and 2) noise-like (shares have a random noise-like appearance). In the previous work, we have proposed PVSS based Randomized VSS (RVSS) scheme to recover the secret image by hiding random data into the shares. RVSS achieves the maximum contrast of 70–80% and 70–90% for meaningful and noise-like shares respectively. In this paper, we propose a novel PVSS based super-resolution technique to improve the contrast of RVSS scheme. The experimental results showed that the proposed scheme achieves the contrast of 70–80% for meaningful shares and 99% for noise-like shares. Also, proposed scheme recovers the secret image free from blocking artifacts, for noise-like shares.
Video Captioning using Deep Learning: An Overview of Methods, Datasets and Metrics

In recent years, automatically generating natural language descriptions for videos has created a lot of focus in computer vision and natural language processing research. Video understanding has several applications such as video retrieval and indexing etc., but the video captioning is the quite challenging topic because of the complex and diverse nature of video content. However, the understanding between video content and natural language sentence remains an open problem to create several methodology to better understand the video and generate the sentence automatically. Deep learning methodologies have increased great focus towards video processing because of their better performance and the high-speed computing capability. This survey discusses various methods using the end-to-end framework of encoder-decoder network based on deep learning approaches to generate the natural language description for video sequences. This paper also addresses the different dataset used for video captioning and image captioning and also various evaluation parameters used for measuring the performance of different video captioning models.
From Conventional Machine Learning to AutoML

Machine Learning has enabled conspicuous progress over the past decade on various areas, such as image analysis, computer vision, natural language processing, quantitative finance, etc. This paper presents an overview of Machine Learning algorithms from conventional Machine Learning to Deep Learning then to Automated Machine Learning (AutoML). In the first two sections, a few classical and distinct algorithms will be discussed in the same structure: 1) the principle of algorithm, 2) specific applications or improvements, and 3) contributions or limitations. At the last section, the state-of-the-art AutoML algorithms will be briefly introduced along with their innovation, which achieve unexpected higher efficiency. It turns out that there is a growing interest in AutoML methods. In the future, more and more novel neural architectures will be proposed and applications to real-world scenario will be expanded.
Wearables-Driven Freeform Handwriting Authentication

With the ubiquity of handwriting in everyday tasks, it is surprising that existing avenues for handwriting authentication remain largely out of reach for the average individual or organization. Current solutions often rely on expensive or specialized equipment, and most existing research focuses on signatures rather than freeform handwriting. This limits the applicability of such technology to a narrow range of scenarios. In this paper, we argue that wearable devices might make handwriting authentication scalable and affordable. We design and evaluate two wearables-driven freeform handwriting authentication systems, one centered on a deep neural network and the other using human-engineered features. Our authentication systems are thoroughly tested across three writing experiments (involving 53 participants) that were carefully mapped to typical writing scenarios. We show the best performing configuration to attain an equal error rate of 5.51%, suggesting the potential of this modality for use in a multi-modal authentication system. To evaluate how our authentication systems perform against attacks by determined attackers, we developed and evaluated two impostor attacks that correspond to highly likely attack vectors. We then show that certain authentication system configurations are resistant to the attack. This paper represents an important step toward consumer ready wearables-driven freeform handwriting authentication.
ReType

When a user needs to reposition the cursor during text editing, this is often done using the mouse. For experienced typists especially, the switch between keyboard and mouse can slow down the keyboard editing workflow considerably. To address this we propose ReType, a new gaze-assisted positioning technique combining keyboard with gaze input based on a new 'patching' metaphor. ReType allows users to perform some common editing operations while keeping their hands on the keyboard. We present the result of two studies. A free-use study indicated that ReType enhances the user experience of text editing. ReType was liked by many participants, regardless of their typing skills. A comparative user study showed that ReType is able to match or even beat the speed of mouse-based interaction for small text edits. We conclude that the gaze-augmented user interface can make common interactions more fluent, especially for professional keyboard users.
Smart and Fermented Cities

What makes a city meaningful to its residents? What attracts people to live in a city and to care for it? Today, we might see such questions as concerns for HCI, given the emerging agendas of smart and connected cities, IoT, and ubiquitous computing: city residents' perceptions of and attitudes towards smart city technologies will play a role in technology acceptance. Theories of "placemaking" from humanist geography and urban planning address themselves to such concerns, and they have been taken up in HCI and urban informatics research. This theory offers ideas for developing community attachment, heightening the legibility of the city, and intensifying lived experiences in the city. We add to this body of research with an analysis of several initiatives of City Yeast, a community-based design collective in Taiwan that proposes the metaphor of fermentation as an approach to placemaking. We unpack how this approach shapes their design practice and link its implications to urban informatics research in HCI. We suggest that smart cities can also be pursued by leveraging the knowledge of city residents and helping to facilitate their participation in acts of perceiving, envisioning, and improving their local communities, including but not limited to smart and connected technologies.
Dynamic Network Plaid

Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram.
Highlight-assisted nighttime vehicle detection using a multi-level fusion network and label hierarchy

Nighttime vehicle detection plays an essential role in Automatic Driving System (ADS) and Driver Assistance System (DAS). However, the visual features of the vehicle at night are undistinguishable due to low illumination and may bring significant challenges to the vision-based method. Vehicle highlight information including vehicle lights and the correspondingly reflected lights is high-confident visual features at night. Thus a detection system effectively using vehicle highlight information can gain large improvements. In this paper, we propose a novel nighttime vehicle detection framework with assistance from the vehicle highlight information. Firstly, we generate a fine-grained vehicle highlight detector and create the vehicle label hierarchy to enlarge the inter-class difference and reduce the intra-class gap. Then, we propose a feature aggregation mechanism to combine multi-scale highlight features and the vehicle’s visual features to take advantages of the vehicle highlight position hints. With the novel vehicle highlight aggregation mechanism, the performance of our method goes beyond that of the state-of-the-art. Also, our method has gained improvements when transferring our method to mainstream frameworks, indicating that our approach is both effective and general.
Ranked-List Visualization

Visualization of ranked lists is a common occurrence, but many in-the-wild solutions fly in the face of vision science and visualization wisdom. For example, treemaps and bubble charts are commonly used for this purpose, despite the fact that the data is not hierarchical and that length is easier to perceive than area. Furthermore, several new visual representations have recently been suggested in this area, including wrapped bars, packed bars, piled bars, and Zvinca plots. To quantify the differences and trade-offs for these ranked-list visualizations, we here report on a crowdsourced graphical perception study involving six such visual representations, including the ubiquitous scrolled barchart, in three tasks: ranking (assessing a single item), comparison (two items), and average (assessing global distribution). Results show that wrapped bars may be the best choice for visualizing ranked lists, and that treemaps are surprisingly accurate despite the use of area rather than length to represent value.