Reviewerly/exHarmony huggingface dataset:
A Parallel Algorithm for Subgraph Isomorphism

In different application fields, such as biology, databases, social networks and so on, graphs are a widely adopted structure to represent the data. In these fields, a relevant problem is the detection and the localization of structural patterns within very large graphs; such a problem, formalized as subgraph isomorphism, has been proven to be NP-Complete in the general case. Moreover, the continuously growing size of the graphs to face, actually of hundred thousands of nodes, is making the problem even more challenging also for the most efficient algorithms in the state of the art, requiring days or weeks of computational time. This huge amount of time is also consequence of the fact that most of the algorithms do not exploit any kind of parallelism, even if the problem is suitable to be solved adopting parallel approaches. In this paper we present a new parallel algorithm for subgraph isomorphism, namely VF3P, based on a redesign of the well known algorithm VF3. The effectiveness of VF3P has been experimentally proven on a publicly available dataset of very large graphs, confirming that the algorithm is able to efficiently scale w.r.t. the number of used CPUs without affecting the memory usage.
Tightly coupled multi-group threshold secret sharing based on Chinese Remainder Theorem

(t,n)-Threshold secret sharing ((t,n)-SS) scheme is a fundamental cryptographic primitive. As a special (t,n)-SS, a Multi-Level threshold Secret Sharing scheme (MLSS) divides shares into different levels. Shares at higher levels can be used at lower ones but shares of lower levels are invalid at higher ones. However, MLSS is limited in applications and vulnerable to Illegal Participant (IP) attack and t-Share Capture (SC) attack. Therefore, the paper first extends the notion of MLSS to multi-group threshold secret sharing (MGSS) to accommodate wider application scenarios. In order to cope with the 2 attacks, the paper then proposes a tightly coupled MGSS scheme based on Chinese Remainder Theorem. In the scheme, a shareholder, with only one private share, is allowed to participate in secret reconstruction of different groups. Moreover, when sufficient shareholders collaborate to recover the secret in a group, they first form a tightly coupled subgroup by constructing a randomized component each so that the secret can be recovered only if each participant has valid share and actually participates in secret reconstruction. Analyses show that the proposed scheme is capable of thwarting IP and SC attacks. Besides, the scheme is more flexible and popular in applications compared with MLSS scheme.
Constant time recovery in Azure SQL database

Azure SQL Database and the upcoming release of SQL Server introduce a novel database recovery mechanism that combines traditional ARIES recovery with multi-version concurrency control to achieve database recovery in constant time, regardless of the size of user transactions. Additionally, our algorithm enables continuous transaction log truncation, even in the presence of long running transactions, thereby allowing large data modifications using only a small, constant amount of log space. These capabilities are particularly important for any Cloud database service given a) the constantly increasing database sizes, b) the frequent failures of commodity hardware, c) the strict availability requirements of modern, global applications and d) the fact that software upgrades and other maintenance tasks are managed by the Cloud platform, introducing unexpected failures for the users. This paper describes the design of our recovery algorithm and demonstrates how it allowed us to improve the availability of Azure SQL Database by guaranteeing consistent recovery times of under 3 minutes for 99.999% of recovery cases in production.
EmotoTent

EmotoTent is an interactive socio-emotional learning system developed in response to escalating levels of violence, inequality and marginalization in schools seen in the early 21st Century. The system is inspired by advances in biosensing wearables, tattoo displays, brain sensors, robotic agents, artificial intelligence (AI), gestural interaction and 3D holographic displays. By 2030, technological advances will enable us to prototype and investigate questions related to experiential and embodied emotional learning; emotion-based human-computer interaction, affective biosensing, empathetic AI agents, and 3D interactive holographic environments. We envision EmotoTent as a modular, emotion-sensing Holodeck. In the EmotoTent program children learn and practice emotion regulation and empathy with peers, pets and a robotic dog agent in ways that are experiential, embodied and playful. We propose EmotoTent as a core element of a K-6 socio-emotional learning curriculum designed to improve school culture through the enhancement of children's ability to regulate emotions and interact with human and non-human species with empathy and compassion. Enhancing these qualities has been shown to lead to reductions in violence and bullying, racism, gender inequality and other forms of marginalization. We predict that the EmotoTent socio-emotional learning program will improve school cultures and create a foundation for children's lifelong well-being.
Discriminative representation learning for person re-identification via multi-loss training

The identification model that employs softmax loss to minimize person identity classification errors has gradually gained popularity in person re-identification community due to its easy implementations. However, the softmax loss only encourages the separation of different identities. The intra-class differences caused by large view variations such as spatial misalignment and human pose change are not considered in the model training process. In this paper, we present a hybrid deep model that combines multiple loss functions to handle this problem. Specifically, the multi-loss function contains three terms, namely softmax loss, center loss, and a novel loss called inter-center loss. The center loss penalizes the distance between deep features and their center, aiming to reduce intra-class differences. The inter-center loss maximizes the distances between different class centers, aiming to further enlarge inter-class separation. Extensive experiments conducted on three public benchmark datasets including Market1501, CUHK03, and DukeMTMC-reID demonstrate the effectiveness of our method.
Together Together

There is a growing concern that the pervasive individualized, digital means for connecting with others is challenging family togetherness, such as Sherry Turkle expresses it in her book entitled "Alone Together". As a response to this concern, we explore a new direction for designing technologies supporting families being 'Together Together' through using separate activities as drivers for shared activities in designing for domestic family life. The direction derives from the use of Participatory Design techniques, engaging six families in a series of in-situ workshops, envisioning how technology can support shared domestic family experiences. We explore this design space through the design of the STORIES prototype and we present the results from in-situ deployments of the STORIES prototype. We conclude that designing for a combination of separate and shared family activities is a promising approach in designing for Family Togetherness.
Large Scale Graph Mining with G-Miner

This Demo presents G-Miner, a distributed system for graph mining. The take-aways for Demo attendees are: (1) a good understanding of the challenges of various graph mining workloads; (2) useful insights on how to design a good system for graph mining by comparing G-Miner with existing systems on performance, expressiveness and user-friendliness; and (3) how to use G-Miner for interactive graph analytics.
Pushing the Boundaries of Participatory Design with Children with Special Needs

Despite its inherent challenges, participatory design (PD) has unique benefits when designing technology for children, especially children with special needs. Researchers have developed a multitude of PD approaches to accommodate specific populations. However, a lack of understanding of the appropriateness of existing approaches across contexts presents a challenge for PD researchers. This workshop will provide an opportunity for PD researchers to exchange and reflect on their experiences of designing with children with special needs. We aim to identify, synthesize and collate PD best practices across contexts and participant groups.
Dynamics of Wideband Time-Delayed Optoelectronic Oscillators With Nonlinear Filters

We present a theoretical and experimental study of an optoelectronic oscillator featuring a cubic-nonlinear filter in the feedback loop. In this architecture, the nonlinearity introduces an additional timescale that leads to the emergence of complex behavior and multiscale dynamics, ultimately leading to chaos as the gain is increased. A complete bifurcation analysis is performed and successfully compared to experimental measurements. We expect this class of systems to emulate novel functionalities for analog signal processing based on time-delayed optoelectronic oscillators.
SNOW: Semi-Supervised, Noisy And/Or Weak Data For Deep Learning In Digital Pathology

Digital pathology produces a lot of images. For machine learning applications, these images need to be annotated, which can be complex and time consuming. Therefore, outside of a few benchmark datasets, real-world applications often rely on data with scarce or unreliable annotations. In this paper, we quantitatively analyze how different types of perturbations influence the results of a typical deep learning algorithm by artificially weakening the annotations of a benchmark biomedical dataset. We use classical machine learning paradigms (semi-supervised, noisy and weak learning) adapted to deep learning to try to counteract those effects, and analyze the effectiveness of these methods in addressing different types of weakness.
Polynomial activation neural networks: Modeling, stability analysis and coverage BP-training

In this paper, firstly, a class of deep (or multi-layer) neural networks with polynomial activation functions (or polynomial activation neural networks: PANN) are created, while feed-forward and recurrent architectures and their nonlinear difference modeling are explicated. PANN’s relationships with conventional deep neural networks with sigmoid activation functions are discussed briefly by means of Taylor series. Secondly, numerical stability and stabilization of PANN’s are examined, and the stability conditions are derived with bounded-state trajectory inequalities and small-state linear approximation, under small parametrization assumption; stability analysis implication coincides with what we already learnt about neural network pre-training. Thirdly, based on what we term the coverage back-propagation parametrization, pre-training algorithms for PANN with or without activation functions optimization are constructed; particularly, activation function optimization is a new concept of this study, which brings us with more learning flexibility in general neural networks. Finally, nonlinear function fitting is numerically illustrated to show application of PANN, which reveals high generalization capability of linear parameter-varying neural algorithms.
ISEE: An Intelligent Scene Exploration and Evaluation Platform for Large-Scale Visual Surveillance

Intelligent video surveillance (IVS) is always an interesting research topic to utilize visual analysis algorithms for exploring richly structured information from big surveillance data. However, existing IVS systems either struggle to utilize computing resources adequately to improve the efficiency of large-scale video analysis, or present a customized system for specific video analytic functions. It still lacks of a comprehensive computing architecture to enhance efficiency, extensibility and flexibility of IVS system. Moreover, it is also an open problem to study the effect of the combinations of multiple vision modules on the final performance of end applications of IVS system. Motivated by these challenges, we develop an Intelligent Scene Exploration and Evaluation (ISEE) platform based on a heterogeneous CPU-GPU cluster and some distributed computing tools, where Spark Streaming serves as the computing engine for efficient large-scale video processing and Kafka is adopted as a middle-ware message center to decouple different analysis modules flexibly. To validate the efficiency of the ISEE and study the evaluation problem on composable systems, we instantiate the ISEE for an end application on person retrieval with three visual analysis modules, including pedestrian detection with tracking, attribute recognition and re-identification. Extensive experiments are performed on a large-scale surveillance video dataset involving 25 camera scenes, totally 587 hours 720p synchronous videos, where a two-stage question-answering procedure is proposed to measure the performance of execution pipelines composed of multiple visual analysis algorithms based on millions of attribute-based and relationship-based queries. The case study of system-level evaluations may inspire researchers to improve visual analysis algorithms and combining strategies from the view of a scalable and composable system in the future.
Children’s embodied intuitive interaction — Design aspects of embodiment

Intuitive features could make complex products and interfaces easier to use for children, and designing for embodied interactions is considered as one of the ways to make products and interfaces intuitive to use. However, there is lack of empirical study to validate this relationship and to determine how embodiment could be integrated in the design of products and interfaces. This study has explored embodiment for intuitive interaction in children. The research question for the study was: what is the role of design aspects of embodiment in facilitation of intuitive interaction in children in the context of tactile interactions. The study identified the extent to which design aspects of embodiment facilitate intuitive interaction in children. An observational study with 108 children (55 girls and 53 boys) was carried out. Half of them played with physical Jenga and the other half played with a virtual Jenga. The physical Jenga demonstrated more intuitive interactions than the equivalent virtual interface. Physical affordance is the prime contributor to children’s intuitive interaction with physical products while perceived affordance is the prime contributor to children’s intuitive interaction with virtual interfaces. Embodied interactions can be achieved through the following design aspects of embodiment — physical affordances, perceived affordances, scaffolding, emergence and cooperative activity. The study has further provided recommendations to make interfaces embodied and intuitive through the Enhanced Framework for Intuitive Interaction. These findings are significant as they provide insights into children’s embodied and intuitive interactions, which contribute to the broader context of children’s interaction with physical products and virtual interfaces.
Constraints-Based Explanations of Classifications

A main component of many Data Science applications is the invocation of Machine Learning (ML) classifiers. The typical complexity of these classification models makes it difficult to understand the reason for a result, and consequently to assess its trustworthiness and detect errors. We propose a simple generic approach for explaining classifications, by identifying relevant parts of the input whose perturbation would be significant in affecting the classification. In contrast to previous work, our solution makes use of constraints over the data, to guide the search for meaningful explanations in the application domain. Constraints may either be derived from the schema or specified by a domain expert for the purpose of computing explanations. We have implemented the approach for prominent ML models such as Random Forests and Neural Networks. We demonstrate, through examples and experiments, the effectiveness of our solution, and in particular of its novel use of constraints.
An Arabic-Multilingual Database with a Lexicographic Search Engine

We present a lexicographic search engine built on top of the largest Arabic multilingual database, allowing people to search and retrieve translations, synonyms, definitions, and more. The database currently contains about 150 Arabic multilingual lexicons that we have been digitizing, restructuring, and normalizing over 9 years. It comprises most types of lexical resources, such as modern and classical lexicons, thesauri, glossaries, lexicographic datasets, and (bi/)tri-lingual dictionaries. This is in addition to the Arabic Ontology – an Arabic WordNet with ontologically cleaned content, which is being used to reference and interlink lexical concepts. The search engine was developed with the state-of-the-art design features and according to the W3C's recommendation and best practices for publishing data on the web, as well as the W3C's Lemon RDF model. The search engine is publicly available at ( https://ontology.birzeit.edu ).
Pledge: A Private Ledger Based Decentralized Data Sharing Framework

Blockchain startups are basing their business models on disrupting the centralized way of data storage by highlighting the value of data to the public. A distributed approach to storing data is the safer way to prevent attacks is what is being evangelized. GAFA (Google, Apple, Facebook, Amazon) have monopolized data, therefore bringing in the most revenue whilst depriving the data generators of any form of compensation. In our work, we propose a platform where a user can store his/her own personal data. Here, we present to the user the right to decide what happens to their data. These decisions include selling, renting, and deleting data. The deletion of data undermines the fundamentals the blockchain is built on (data immutability). We address the issues of personal data sharing and how a user's right to delete his/her data can be enforced in a blockchain based network such as ours.
‘<i>Smart – not only intelligent!’</i> Co-creating priorities and design direction for ‘smart’ footwear to support independent ageing.

With an ageing population there is a growing need for technology that enables older adults to live independently for longer. The EU Horizon2020 funded MATUROLIFE project is focused on developing solutions that embed smart textiles to support well-being and independence in older adults. The study described here aimed to explore and initiate development of 'smart' footwear embedding assistive technology. A qualitative research strategy was employed including interviews with 37 older adults and co-creation activities with 56 older adults. Participants were recruited from eight European countries (Belgium, France, Germany, Italy, Poland, Spain, Turkey, and United Kingdom). The results detail the specific needs that older adults have in relation to footwear based on the daily activities they take part in. Participants shared their footwear fashion preferences, as well as their priorities for assistive functionality. A set-of co-created priorities and concept ideas are presented that consider how footwear might enable independent ageing.
On the Stability of Feature Selection in the Presence of Feature Correlations

Feature selection is central to modern data science. The 'stability' of a feature selection algorithm refers to the sensitivity of its choices to small changes in training data. This is, in effect, the robustness of the chosen features. This paper considers the estimation of stability when we expect strong pairwise correlations, otherwise known as feature redundancy. We demonstrate that existing measures are inappropriate here, as they systematically underestimate the true stability, giving an overly pessimistic view of a feature set. We propose a new statistical measure which overcomes this issue, and generalises previous work.
Mask Guided Knowledge Distillation for Single Shot Detector

In this paper, we explore the idea of distilling small networks for object detection task. More specifically, we propose a two-stage approach to learn more compact and efficient detectors under the single-shot object detection framework by leveraging knowledge distillation. During the 1st stage, we learn the feature maps of the student model for each of the prediction head from the teacher model. Instead of fitting the whole feature map directly, here we propose the mask guided structure including not only the entire feature map (i.e. global features) but also region features covered by the object (i.e. local features), which can significantly improve the performance of the student network. For the 2nd stage, the ground-truth is used to further refine the performance. Experimental results on PASCAL VOC and KITTI dataset demonstrate the effectiveness of our proposed approach. We achieve 56.88% mAP on VOC2007 at 143 FPS with the backbone of 1/8 VGG16.
Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction

Mortality prediction is one of the essential tasks in medical data mining and is significant for inferring clinical outcomes. With a large number of medical notes collected from hospitals, there is an urgent need for developing effective models for predicting mortality based on them. In contrast to structured electronic health records, medical notes are unstructured texts written by experienced caregivers and contain more complicated information about patients, posing more challenges for modeling. Most previous studies rely on tedious hand-crafted features or generating indirect features based on some statistical models such as topic modeling, which might incur information loss for later model training. Recently, some deep models have been proposed to unify the stages of feature construction and model training. However, domain concept knowledge has been neglected, which is important to gain a better understanding of medical notes. To address the above issues, we propose novel Knowledge-aware Deep Dual Networks (K-DDN) for the text-based mortality prediction task. Specifically, a simple deep dual network is first proposed to fuse the representations of medical knowledge and raw text for prediction. Afterward, we incorporate a co-attention mechanism into the basic model, guiding the knowledge and text representation learning with the help of each other. Experimental results on two publicly real-world datasets show the proposed deep dual networks outperform state-of-the-art methods and the co-attention mechanism can further improve the performance.
Anisotropic diffusion filtering method with weighted directional structure tensor

The anisotropic diffusion filtering algorithm has excellent smoothing performance for medical images, but the normal diffusion filtering algorithm will blur the edges and details. In this paper, we construct weighted directional structural tensor (WDT) and propose an anisotropic diffusion filtering method based on the WDT to overcome the fuzzy appearance drawback. The proposed algorithm first constructs the directional structure tensor based on the traditional structure tensor, and then add the definition of the weight in the non-local mean to construct WDT. To further protect the edges and small structural features of the image, we also set the diffusion weighting coefficient according to the eigenvalues of DWT to construct directed diffusion tensor. Experimental results indicate that the proposed method shows better performance than other filtering methods, and greatly improves the image sharpness, presents better image details and maintains the edge contours while denoising.