Reviewerly/exHarmony huggingface dataset:
MuCAI'20

Recently, conversational systems have seen a significant rise in demand due to modern commercial applications using systems such as Amazon's Alexa, Apple's Siri, Microsoft's Cortana and Google Assistant. The research on multimodal chatbots is a widely underexplored area, where users and the conversational agent communicate by natural language and visual data. Conversational agents are now becoming a commodity as a number of companies push for this technology. The wide use of these conversational agents exposes the many challenges in achieving more natural, human-like, and engaging conversational agents. The research community is actively addressing several of these challenges: how are visual and text data related in user utterances? How to interpret the user intent? How to encode multimodal dialog status? What are the ethical and legal aspects of conversational AI? The Multimodal Conversational AI workshop will be a forum where researchers and practitioners share their experiences and brainstorm about success and failures in the topic. It will also promote collaboration to strengthen the conversational AI community at ACM Multimedia.
Vehicular networks for combating a worldwide pandemic: Preventing the spread of COVID-19

As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people's social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids. Despite the advance of technological developments, we, as researchers, have witnessed that several issues need further investigation for a better response to a pandemic outbreak. Therefore, researchers recently started developing ideas to stop or at least reduce the spread of the pandemic. While there have been some prior works on wireless networks for combating a pandemic scenario, vehicular networks and their potential bottlenecks have not yet been fully examined. Furthermore, the vehicular scenarios can be identified as the locations, where the social distancing is mostly violated. With this motivation, this article provides an extensive discussion on vehicular networking for combating a pandemic. We provide the major applications of vehicular networking for combating COVID-19 in public transportation, in-vehicle diagnosis, border patrol and social distance monitoring. Next, we identify the unique characteristics of the collected data in terms of privacy, flexibility and coverage, then highlight corresponding future directions in privacy preservation, resource allocation, data caching and data routing. We believe that this work paves the way for the development of new products and algorithms that can facilitate the social life and help controlling the spread of the pandemic.
Large Scale Hierarchical Anomaly Detection and Temporal Localization

Abnormal event detection is a non-trivial task in machine learning. The primary reason behind this is that the abnormal class occurs sparsely, and its temporal location may not be available. In this paper, we propose a multiple feature-based approach for CitySCENE challenge-based anomaly detection. For motion and context information, Res3D and Res101 architectures are used. Object-level information is extracted by object detection feature-based pooling. Fusion of three channels above gives relatively high performance on the challenge Test set for the general anomaly task. We also show how our method can be used for temporal localisation of the abnormal activity event in a video.
Immersive Imaging Technologies: From Capture to Display

New immersive imaging technologies enable creating multimedia systems that would increase the viewer presence and provide an immersive experience. This half-day tutorial aims to give an overview of these new immersive imaging systems and help the participants understand the content creation and delivery pipeline for the immersive imaging technologies. The tutorial will go over the full imaging pipeline, from camera setup for content capture, through content compression / streaming, to content display and related perceptual studies.
Document anonymization for border guards and immigration services

The current capabilities and capacities of border guards and immigration services can be enhanced using technologies that automate the analysis of travel, identity and breeder documents in order to detect fraud. These technologies can be relevant for countering emerging threats in document and identity verification (e.g., forged documents, impostor fraud, morphed faces) at both manual and highly automated border control points (both in the first and in the second line) and in the issuance process of genuine documents. The travel documents (e.g., passports) and breeder documents (e.g., birth certificates) contain personal information, such as name, date of birth and national number. The personal information must be well protected and a data breach must be avoided at all times. One of the ways to protect the personal data is to minimize the sharing of personal data. Anonymization removes the personal information (e.g., by replacing the personal information by a black bar) and can therefore be used to minimize the sharing of personal data. This paper describes the tool that assists border guards and immigration services for the anonymization of travel and breeder documents. The tool consists of a graphical user interface, document detection, keyword recognition, face detection, number detection, barcode detection and masking of personal data. The results show that only 10 annotated images are needed to reach a keyword detection accuracy of 96% and anonymization sensitivity of 93% of the related personal data.
Batch-sequential design and heteroskedastic surrogate modeling for delta smelt conservation

Delta smelt is an endangered fish species in the San Francisco estuary that have shown an overall population decline over the past 30 years. Researchers have developed a stochastic, agent-based simulator to virtualize the system with the goal of understanding the relative contribution of natural and anthropogenic factors that might play a role in their decline. However, the input configuration space is high dimensional, running the simulator is time-consuming, and its noisy outputs change nonlinearly in both mean and variance. Getting enough runs to effectively learn input–output dynamics requires both a nimble modeling strategy and parallel evaluation. Recent advances in heteroskedastic Gaussian process (HetGP) surrogate modeling helps, but little is known about how to appropriately plan experiments for highly distributed simulation. We propose a batch sequential design scheme, generalizing one-at-a-time variance-based active learning for HetGP, as a means of keeping multicore cluster nodes fully engaged with runs. Our acquisition strategy is carefully engineered to favor selection of replicates which boost statistical and computational efficiency when training surrogates to isolate signal from noise. Design and modeling are illustrated on a range of toy examples before embarking on a large-scale smelt simulation campaign and downstream high-fidelity input sensitivity analysis.
AI at the Disco

Human activity recognition (HAR) has grown in popularity as sensors have become more ubiquitous. Beyond standard health applications, there exists a need for embedded low cost, low power, accurate activity sensing for entertainment experiences. We present a system and method of using a deep neural net for HAR using low-cost accelerometer-only sensor running at 0.8Hz to preserve battery power. Despite these limitations, we demonstrate an accuracy at 94.79% over 6 activity classes with an order of magnitude less data. This sensing system conserves power further by using a connectionless reading---embedding accelerometer data in the Bluetooth Low Energy broadcast packet---which can deliver over a year of human-activity recognition data on a single coin cell battery. Finally, we discuss the integration of our HAR system in a smart-fashion wearable for a live two night deployment in an instrumented night club.
A Penalty-Aware Cloud Monitoring System based on Blockchains

Today, traded cloud services are described by service level agreements that specify the obligations of providers such as availability or reliability. Violations of service level agreements lead to penalty payments. The recent development of prominent cloud platforms such as the re-design of Amazon's spot marketspace underpins a trend towards dynamic cloud markets where consumers migrate their services continuously to different marketspaces and providers to reach a cost-optimum. This leads to a heterogeneous IT infrastructure and consequently aggravates the monitoring of the delivered service quality. Hence, there is a need for a transparent penalty management system, which ensures that consumers automatically get penalty payments from providers in case of service violations.
Semi-supervised Uncertain Linear Discriminant Analysis

Linear discriminant analysis (LDA) is a popular supervised method for dimensionality reduction for its simplicity and effectiveness. However, its performance may significantly deteriorate in the situation that labeled training samples are very scarce and many semi-supervised methods are presented to solve the problem with a large amount of unlabeled samples available. In this paper, inspired by the uncertainty idea, we propose a semi-supervised uncertain linear discriminant analysis (SULDA). First, we present a fractional-step label propagation to reliably label the unlabeled samples. Secondly, we use all training samples and their labels to construct double-layer Gaussian mixture models. Finally, we utilize the originally labeled samples and the obtained models to define expected between-class and within-class scatter matrices. Experimental results illustrate that our method is superior to some state-of-the-art semi-supervised methods with respect to the discriminative power.
FastRCA-Seq: An efficient approach for extracting hierarchies of multilevel closed partially-ordered patterns

Discovering concise representations of sequential patterns in sequential data is a well-established data mining task. Recently, Nica et al. have put forward an original approach RCA-Seq for directly extracting a hierarchy of multilevel closed partially-ordered patterns (MCPO-patterns) from a sequence database within the Relational Concept Analysis (RCA) framework. RCA-Seq has been applied successfully to small (∼1,000 sequences) but interesting real hydro-ecological datasets. However, RCA-Seq only focuses on providing comprehensible results to the detriment of performance. To improve the performance of RCA-Seq, we propose a new approach FastRCA-Seq that stems from RCA-Seq, and whose contributions are beneficial for two fields: Formal Concept Analysis, namely the RCA extension, and sequential pattern mining. FastRCA-Seq spans two key steps: the exploration of sequential data based on RCA, and the extraction of MCPO-patterns by navigating the RCA result. Firstly, our approach introduces an effective RCA implementation based on bit-array representations, bitwise operations, parallel computing, and several new properties of RCA that may prevent expensive computations. In addition, we state the bottleneck of RCA. Secondly, FastRCA-Seq is a self-contained approach for directly and efficiently mining hierarchies of MCPO-patterns from sequential data. We assess FastRCA-Seq on various benchmark datasets, precisely Gazelle, Kosarak, and FIFA. The results show that FastRCA-Seq outperforms RCA-Seq in terms of execution time (in average ∼169 times faster) and memory usage (in average with ∼42% less) while preserving the benefits of interpretability and usability of results by stakeholders.
Adaptive Attributed Network Embedding for Community Detection

Community detection, which discovers densely-connected groups of nodes in networks, is a fundamental task in machine learning and data mining. Compared with plain network, community detection in attributed network presents more challenges. Several recent embedding-based methods have achieved promising community detection performance on some real attributed networks. However, there is limited understanding of how to effectively learn the combination of heterogeneity in the joint space of topology and attribute in unsupervised scenarios. In this paper, we propose an end-to-end network embedding method. By employing the high order graph convolutional networks, our method encodes the topological structure and node attributes to learn compact representations, i.e., community membership. The decoder on the other side, we reconstruct the global topological structure based on the learned community membership and stochastic block model. We further employ a self-training module, which takes the “confident” link assignments as soft labels to guide the optimizing procedure. Experiments show our method has achieved the sate-of-the-art performance on three popular datasets.
Where’s Wally: A Gigapixel Image Study for Face Recognition in Crowds

Several devices are capable of capturing images with a large number of people, including those of high resolution known as gigapixel images. These images can be helpful for studies and investigations, such as finding people in a crowd. Although they can provide more details, the task of identifying someone in the crowd is quite challenging and complex. In this paper, we aim to assist the work of a human observer with larger images with crowds by reducing the search space for several images to a ranking of ten images related to a specific person. Our model collects faces in a crowded gigapixel image and then searches for people using three different poses (front, right and left). We built a handcraft dataset with 42 people to evaluate our method, achieving a recognition rate of 69% in the complete dataset. We highlight that, from the 31% "not found" among the top ten in the ranking, many of them are very close to this boundary and, in addition, 92% of non-matched are occluded by some accessory or another face. Experimental results showed great potential for our method to support a human observer to find people in the crowd, especially cluttered images, providing her/him with a reduced search space.
Evaluating tool support for embedded operating system security

Embedded systems are more and more connected to a variety of networks, which increases their attack surface. At the same time, more and more objects are augmented with embedded systems, which increases the potential impact of attacks. Cybersecurity must therefore be taken into account while designing and developing embedded software systems. While there are multiple complementary facets to the security of such systems, we focus on embedded operating system security, which is critical to build secure applications. In order to evaluate the applicability of the many available cybersecurity techniques and tools, we need to define a relevant case study. Given that a system's inputs are especially vulnerable, we have specified a fictive device driver, which we have both modeled in UML and implemented in C. We report here on the initial application of a couple of techniques to analyse the security of this device driver at the model and code levels.
Using scaffolding to formalize digital coach support for low-literate learners

Abstract In this study, we attempt to specify the cognitive support behavior of a previously designed embodied conversational agent coach that provides learning support to low-literates. Three knowledge gaps are identified in the existing work: an incomplete specification of the behaviors that make up ‘support,’ an incomplete specification of how this support can be personalized, and unclear speech recognition rules. We use the socio-cognitive engineering method to update our foundation of knowledge with new online banking exercises, low-level scaffolding and user modeling theory, and speech recognition. We then refine the design of our coach agent by creating comprehensive cognitive support rules that adapt support based on learner needs (the ‘Generalized’ approach) and attune the coach’s support delay to user performance in previous exercises (the ‘Individualized’ approach). A prototype is evaluated in a 3-week within- and between-subjects experiment. Results show that the specified cognitive support is effective: Learners complete all exercises, interact meaningfully with the coach, and improve their online banking self-efficacy. Counter to hypotheses, the Individualized approach does not improve on the Generalized approach. Whether this indicates suboptimal operationalization or a deeper problem with the Individualized approach remains as future work.
Structure relationship classification for the recognition of mathematical expression handwritten in Arabic

An essential issue for the recognition of handwritten mathematical formulas is the identification of the structural relationships between each pairs of adjacent symbols that compose the entire mathematical formula. The classification of the structural relationship is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After the combination of extracted features, we classify the relationship into six different classes using four different classifiers in order to determine the most efficient. In our proposed system, a support vector machine (SVM) classifier, Random Forest, Adaboost and KNN are employed. Experimental results show that our features give promising results.
Active Query of Private Demographic Data for Learning Fair Models

Learning a fair prediction model is an important research problem with profound societal impacts. Most approaches assume free access to the sensitive demographic data, whereas these data are becoming restricted to use by privacy regulations. Existing solutions are broadly based on multi-party computation or demographic proxy, but each direction has its own limits in certain scenarios.
Utilizing Agile Approach for Well Construction Planning

Abstract An Agile well construction planning method was implemented to aid in a significant cycle time reduction for the completion of all necessary steps in technical design and review in well plan delivery. By utilizing this novel method, 8 fit for purpose well plan packages were completed and delivered in 3.5 weeks. While traditional process is often characterized by multiple, interdependent sequential steps and as such, can typically take 1-2 months for a single well. This prolonged timeframe is incompatible with an ever-fluctuating market environment and as such, a more rapid, well delivery method is required in order to deliver significant cost savings in a competitive price environment. The Agile well planning approach deployed incorporated iterative and incremental development processes enabling decision making to be employed efficiently with a high degree of quality to any responding changes. This novel planning approach is based on working in self-organized, multi- disciplinary team, responsible for developing a project incrementally by periodically reviewing, assessing and applying any required course corrections to meet the project goal. It works by splitting the whole project into parallel tasks called ‘swimlanes’ that incorporate clearly defined individual timeframes, entry and exit criteria for each work item. This is opposed to the traditional waterfall method of delivering a final product only at the end of the planning phase, which may require lengthy rework if market and operational conditions change. The Agile well planning process resulted in the delivery of high quality and up to date well programs through synergies found in dynamic integration of geological, economical and engineering data. Interactive review provided us with a quick 1-day response to any changes while planning without major reworking of past material. The result of applying these best practices helped in improving well planning documentation delivery pace from 1.5 wells per month to 8 well per months. This was achieved by the full integration of different disciplines, avoiding repetitive tasks and providing management more influence on the inflight product. Therefore, application of the novel Agile management approach in well construction planning allows more rapid well delivery whilst still maintaining quality in the final deliverables.
ICT in the Education System: A View from Slovakia

This chapter will provide information about the education system in Slovakia, major education policy documents and priorities, the current ICT (Information and Communications Technology) infrastructure, open educational resources, data about ICT competencies and skills of students, teachers’ skills and professional development and finally about the outcomes and challenges. The introductory part will give an overview of the education system in Slovakia as a whole. The policy perspective section will look at the institutions, legal acts and newly developed National Programme for Upbringing and Education Development in the Slovak Republic until 2027 (called “Learning Slovakia”). It was published in September 2017 and focuses on ICT issues. In the infrastructure section, the situation in the field of ICT (hardware, software, connectivity) at different educational stages will be described. The ICT indicators in education in Slovakia will be compared with the EU average. The next part will look at educational resources in Slovakia at the EU, national and university level. This will present different initiatives, projects and portals with case studies. The part related to students´ ICT competences will outline ICT use for learning purposes in comparison to the EU average in addition to initiatives and activities towards ICT skill development. In the section looking at teachers´ professional development, there will be a comparison of teachers’ ICT skills and needs with EU countries and the OECD average. In the final part, digital literacy challenges will be addressed.
Software Implementation of CRA and TRA to Recover the AES-128 Key using Side-Channel Signals with Python3

In this scientific paper is presented a software implementation of two side-channel radio attacks: CRA - Correlation Radio Attack and TRA - Template Radio Attack. The main goal is to recover the AES-128 key from an unknown system. Both attacks are developed in Python3 using a Linux-based computer. The algorithm uses Probability Density Functions and the Hamming-weight model. The results are calculated by the Partial Guessing Entropy. The software is run on the CPU.
Euge: Effective Utilization of GPU Resources for Serving DNN-Based Video Analysis

Deep Neural Network (DNN) has been widely adopted in video analysis application. The computation involved in DNN is more efficient on GPUs than on CPUs. However, recent serving systems involve the low utilization of GPU, due to limited process parallelism and storage overhead of DNN model. We propose Euge, which introduces multi-process service (MPS) and model sharing technology to support effective utilization of GPU. With MPS technology, multiple processes overcome the obstacle of GPU context and execute DNN-based video analysis on one GPU in parallel. Furthermore, by sharing the DNN-based model among threads within a process, Euge reduces the GPU memory overhead. We implement Euge on Spark and demonstrate the performance of vehicle detection workload.