Reviewerly/exHarmony huggingface dataset:
MoQA – A Multi-modal Question Answering Architecture

Multi-Modal Machine Comprehension (M3C) deals with extracting knowledge from multiple modalities such as figures, diagrams and text. Particularly, Textbook Question Answering (TQA) focuses on questions based on the school curricula, where the text and diagrams are extracted from textbooks. A subset of questions cannot be answered solely based on diagrams, but requires external knowledge of the surrounding text. In this work, we propose a novel deep model that is able to handle different knowledge modalities in the context of the question answering task. We compare three different information representations encountered in TQA: a visual representation learned from images, a graph representation of diagrams and a language-based representation learned from accompanying text. We evaluate our model on the TQA dataset that contains text and diagrams from the sixth grade material. Even though our model obtains competing results compared to state-of-the-art, we still witness a significant gap in performance compared to humans. We discuss in this work the shortcomings of the model and show the reason behind the large gap to human performance, by exploring the distribution of the multiple classes of mistakes that the model makes.
Classification of Remote Sensing Imagery Based on Density and Fuzzy c-Means Algorithm

The goal of data clustering is to divide a set of data into different clusters, so that the data in the same cluster show some similar characteristics. There are many clustering methods for satellite image segmentation, such as k-means, c-means, iso-data, minimum distance algorithms. Each method has certain advantages and disadvantages, but generally they are based on brightness value to divide the pixels of the image in to clusters. Actually, the probability of occurrence of frequency of appearance of pixel has certain effects on clustering results. In this article, the authors propose a method for clustering satellite imagery based on density. It consists of two main steps: find cluster centroid using density and data clustering using fuzzy c-Means algorithm (DFCM). The results obtained in this study can be used to potentially improve classification accuracy of satellite image.
Smart Monitoring of User and Home Environment: The Health@Home Acquisition Framework

This work has been developed within the framework of the Italian smart city project “Health@Home (H@H)”. The main goal is the development of a joint network (heterogeneous devices, both biomedical and home automation) to monitor the user’s health conditions within the home environment, together with dedicated services from the measured quantities. In particular, H@H follows the context of the Active and Assisted Living environment to improve the well-being of elderly giving support to the users at home. In this paper, the authors describe the implemented final prototype software architecture implemented, the measuring protocol used in resting conditions and the implementation of three user services, providing real-time feedback about the user health status.
Large Universe Subset Predicate Encryption Based on Static Assumption (Without Random Oracle)

In a recent work, Katz et al. (CANS'17) generalized the notion of Broadcast Encryption to define Subset Predicate Encryption (SPE) that emulates subset containment predicate in the encrypted domain. They proposed two selective secure constructions of SPE in the small universe settings. Their first construction is based on q-type assumption while the second one is based on DBDH. Both achieve constant size secret key while the ciphertext size depends on the size of the privileged set. They also showed some black-box transformation of SPE to well-known primitives like WIBE and ABE to establish the richness of the SPE structure. This work investigates the question of large universe realization of SPE scheme based on static assumption without random oracle. We propose two constructions both of which achieve constant size secret key. First construction $$\mathsf {SPE}_1$$ , instantiated in composite order bilinear groups, achieves constant size ciphertext and is proven secure in a restricted version of selective security model under the subgroup decision assumption (SDP). Our main construction $$\mathsf {SPE}_2$$ is adaptive secure in the prime order bilinear group under the symmetric external Diffie-Hellman assumption (SXDH). Thus $$\mathsf {SPE}_2$$ is the first large universe instantiation of SPE to achieve adaptive security without random oracle. Both our constructions have efficient decryption function suggesting their practical applicability. Thus the primitives like WIBE and ABE resulting through black-box transformation of our constructions become more practical.
Post-quantum Cryptography and a (Qu)Bit More

Probabilities govern our day to day lives. Undoubtedly, we construct many of our judgments based on assumptions. A scientific example is the case of public-key encryption, where hardness assumptions are the main ingredient of provable security. But, while such clever mathematical ideas mesmerized both researchers and users since the 1970’s, a rather new assumption shakes the cryptographic world: the eventual construction of quantum computers. In this article, we provide the reader with a comprehensive overview regarding post-quantum cryptography. Compared to other well established surveys which underline the importance of designing post-quantum public-key cryptographic algorithms, we stress that symmetric key cryptography should receive the same amount of attention from the scientific community.
The AHA Project: An Evidence-Based Augmented Reality Intervention for the Improvement of Reading and Spelling Skills in Children with ADHD

Students with Attention Deficit Hyperactivity Disorder (ADHD) experience difficulties in maintaining focused attention and remaining on-task and often have comorbid Specific Learning Difficulties (SLD). Given the characteristics of ADHD, interventions for the SLDs should be designed to enhance students’ motivation and engagement. Augmented Reality (AR) can improve students’ outcomes by enhancing attention and motivation. In this direction, the ADHD-Augmented (AHA) project implements an evidence-based intervention, supported by AR, to improve reading and spelling skills for children diagnosed with ADHD.
The Tools of Realisation of the Distant Learning Technologies

The article is related to the organization of educational process with the application of electronic training and distant learning technologies. The issues of establishing and functioning of the unifi ed electronic information and education environment including various instruments and training resources in the educational institution are considered. The means of information and communication technologies, in particular, educational integration platforms which can be used for the development, management and distribution of online materials as a part of the distant training courses are analyzed. The main criteria, essential when choosing the tools for the organization of electronic training are revealed. The analysis of the information development and the functionality of the educational integration platforms from the learning content management systems (LCMS) and the learning management systems (LMS) to up to date personal learning environment (PLE) and virtual learning environment (VLE) is carried out. The issues of the incorporation of the PLE and VLE tools into the LMS are considered, the ways of their integration are set forward.
Volumetric Adversarial Training for Ischemic Stroke Lesion Segmentation

Ischemic stroke is one of the most common and yet deadly cerebrovascular diseases. Identifying lesion area is an essential step for stroke management and outcome assessment. Currently, manual delineation is the gold standard for clinical diagnosis. However, inter-annotator variances and labor-intensive nature of manual labeling can lead to observer bias or potential disagreement of between annotators. While incorporating a computer-aided diagnosis system may alleviate these issues, other challenges such as highly varying shapes and difficult boundaries in the lesion area make the designing of such system non-trivial. To address these issues, we propose a novel adversarial training paradigm for segmenting ischemic stroke lesion. The training procedure involves the main segmentation network and an auxiliary critique network. The segmentation network is a 3D residual U-net that produces a segmentation mask in each training iteration while critique network enforces high-level constraints on the segmentation network to produce predictions that mimic the ground truth distribution. We applied the proposed model on the 2018 ISLES stroke lesion segmentation challenge dataset and achieved competitive results on the training dataset.
Adapting Egocentric Visual Hand Pose Estimation Towards a Robot-Controlled Exoskeleton

The basic idea behind a wearable robotic grasp assistance system is to support people that suffer from severe motor impairments in daily activities. Such a system needs to act mostly autonomously and according to the user’s intent. Vision-based hand pose estimation could be an integral part of a larger control and assistance framework. In this paper we evaluate the performance of egocentric monocular hand pose estimation for a robot-controlled hand exoskeleton in a simulation. For hand pose estimation we adopt a Convolutional Neural Network (CNN). We train and evaluate this network with computer graphics, created by our own data generator. In order to guide further design decisions we focus in our experiments on two egocentric camera viewpoints tested on synthetic data with the help of a 3D-scanned hand model, with and without an exoskeleton attached to it. We observe that hand pose estimation with a wrist-mounted camera performs more accurate than with a head-mounted camera in the context of our simulation. Further, a grasp assistance system attached to the hand alters visual appearance and can improve hand pose estimation. Our experiment provides useful insights for the integration of sensors into a context sensitive analysis framework for intelligent assistance.
Adaptive firefly algorithm based optimized key generation for image security

Cryptography is the most peculiar way to secure data and most of the encryption algorithms are mainly used for textual data and not suitable for transmission data such as images. It is seen that the generation of secure key in Image cryptography has been a challenging task in the way of providing secured key generation for the transmitted data. In order to aid secured key generation in this context, an optimized secret key generation based on Chebyshev polynomial with Adaptive Firefly (FF) optimization technique is proposed. The optimized key is utilized with process of shuffling, diffusion, and swapping to get a better encrypted image. At the receiver end, reverse process is applied with optimized key to retrieve the original input image. The efficiency of our proposed method is assessed by the exhaustive experimental study. The results show that the proposed methodology provided correlation coefficient of 0.21, Number of Pixels Change Rate (NPCR) of 0.996, Unified Average Changing Intensity (UACI) of 0.3346 and Information Entropy of 7.995 as compared with the existing methods.
Interfaces for Science: Conceptualizing an Interactive Graphical Interface

6,849.32 new research journal articles are published every day. The exponential growth of Scientific Knowledge Objects (SKOs) on the Web, makes searches time-consuming. Access to the right and relevant SKOs is vital for research, which calls for several topics, including the visualization of science dynamics. We present an interface model aimed to represent of the relations that emerge in the science social space dynamics, namely through the visualization and navigation of the relational structures between researchers, SKOs, knowledge domains, subdomains, and topics. This interface considers the relationship between the researcher who reads and shares the relevant articles and the researcher who wants to find the most relevant SKOs within a subject matter. This article presents the first iteration of the conceptualization process of the interface layout, its interactivity and visualization structures. It is essential to consider the hierarchical and relational structures/algorithms to represent the science social space dynamics. These structures are not being used as analysis tools, because it is not objective to show the linkage properties of these relationships. Instead, they are used as a means of representing, navigating and exploring these relationships. To sum up, this article provides a framework and fundamental guidelines for an interface layout that explores the social science space dynamics between the researcher who seeks relevant SKOs and the researchers who read and share them.
Speckle Reduction of Ultrasound Image via Morphological Based Edge Preserving Weighted Mean Filter

Medical ultrasound images suffer from inherently generated speckle noise that makes difficult the radiologists to diagnose the diseases. Proper speckle reduction technique is required to improve quality of image which may help the doctor to diagnose the diseases correctly. Speckle removal is, specially, a filtering technique that reduces the amount of speckle noise. But as an effect of filtering, the edges of objects may become blur and fine details within the image may be lost. Many methods have already been proposed to achieve these two requirements. But they do not satisfy both up to the desirable level. In this paper, we propose a novel morphological operation based speckle filter which reduces the effect of speckle noise to the greater extent in one side and keeps detail information in other side. The use of morphological operators helps in extracting the structures present within the images followed by an edge preserving weighted mean filtering method that may help in despeckling of ultrasound images. The proposed technique is applied on the ultrasound simulated data as well as on the real data obtained from the ultrasound machine. The quantitative results and the output images confirm the superiority of the method when compared with some of the other contemporary speckle reduction methods.
Design of Successively Refinable Unrestricted Polar Quantizer

This paper addresses the design of two-stage successively refinable unrestricted polar quantizers for bivariate circularly symmetric sources in the entropy-constrained and fixed-rate cases. The proposed solutions are globally optimal when the thresholds of the magnitude quantizers are confined to finite discretizations of the interval [0, ∞). The algorithm developed for the entropy-constrained case involves a series of stages, including solving the minimum-weight path problem for multiple node pairs in certain weighted directed acyclic graphs. The asymptotical time complexity is O(K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> ), where K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> are the sizes of the sets of possible magnitude thresholds of the coarse and refined unrestricted polar quantizers (UPQs), respectively, while P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> is an upper bound on the number of phase levels in any phase quantizer of the coarse UPQ. The solution algorithm for the fixed-rate case is based on solving a succession of dynamic programming problems for multiple coarse quantizer bins. The time complexity in the fixed-rate case amounts to O(K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ), where N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> is the number of cells of the coarse UPQ and N is the ratio between the number of bins of the fine and coarse UPQs. The extensive experimental results on a bivariate circularly symmetric Gaussian source show the effectiveness of the proposed schemes.
A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion

In Multimedia Internet of Things (IoT), in order to reduce the bandwidth consumption of wireless channels, Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is often used to support the low-bitrate video communication. In this paper, we propose a spatial predictive algorithm which is used to improve the performance of MC-FRUC. The core of the proposed algorithm is a predictive model to split a frame into two kinds of blocks: basic blocks and absent blocks. Then an improved bilateral motion estimation is proposed to compute the Motion Vectors (MVs) of basic blocks. Finally, with the spatial correlation of Motion Vector Field (MVF), the MV of an absent block is predicted based on the MVs of its neighboring basic blocks. Experimental results show that the proposed spatial prediction algorithm can improve both the objective and the subjective quality of the interpolated frame, with a low computational complexity.
Quality Control of Voice Recordings in Remote Parkinson’s Disease Monitoring Using the Infinite Hidden Markov Model

The performance of voice-based systems for remote monitoring of Parkinson's disease is highly dependent on the degree of adherence of the recordings to the test protocols, which probe for specific symptoms. Identifying segments of the signal that adhere to the protocol assumptions is typically performed manually by experts. This process is costly, time consuming, and often infeasible for large-scale data sets. In this paper, we propose a method to automatically identify the segments of signals that violate the test protocol with a high accuracy. In our approach, the signal is first split into variable duration segments by fitting an infinite hidden Markov model (iHMM) to the frames of the signals in the mel-frequency cepstral domain. The complexity of the iHMM is capable of growing jointly with the data allowing us to infer a potentially large (asymptotically infinite) number of different phenomena segmented into different hidden states. Then, we identify the segments that adhere to the test protocol by applying a multinomial naive Bayes classifier to the state indicators of segments. The experimental results show that even by using a small amount of training data, we can achieve around 96% accuracy in identifying short-term protocol violations with a 0.2 s resolution.
Exploring Bias in Primate Face Detection and Recognition

Deforestation and loss of habitat have resulted in rapid decline of certain species of primates in forests. On the other hand, uncontrolled growth of a few species of primates in urban areas has led to safety issues and nuisance for the local residents. Hence, identifying individual primates has become the need of the hour - not only for conservation and effective mitigation in the wild but also in zoological parks and wildlife sanctuaries. Primates and human faces share a lot of common features like position and shape of eyes, nose and mouth. It is worth exploring whether the knowledge of human faces and recent methods learned from human face detection and recognition can be extended to primate faces. However, similar challenges relating to bias in human faces will also occur in primates. The quality and orientation of primate images along with different species of primates - ranging from monkeys to gorillas and chimpanzees will contribute to bias in effective detection and recognition. Experimental results on a primate dataset of over 80 identities show the effect of bias in this research problem.
ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations

With the rise of new laws around privacy and awareness, explanation of automated decision making becomes increasingly important. Nowadays, machine learning models are used to aid experts in domains such as banking and insurance to find suspicious transactions, approve loans and credit card applications. Companies using such systems have to be able to provide the rationale behind their decisions; blindly relying on the trained model is not sufficient. There are currently a number of methods that provide insights in models and their decisions, but often they are either good at showing global or local behavior. Global behavior is often too complex to visualize or comprehend, so approximations are shown, and visualizing local behavior is often misleading as it is difficult to define what local exactly means (i.e. our methods don’t “know” how easily a feature-value can be changed; which ones are flexible, and which ones are static). We introduce the ICIE framework (Interactive Contextual Interaction Explanations) which enables users to view explanations of individual instances under different contexts. We will see that various contexts for the same case lead to different explanations, revealing different feature interactions.
Person, Organization, or Personage: Towards User Account Type Prediction in Microblogs

During the past decade, microblog services have been extensively utilized by millions of business and private users as one of the most powerful information broadcasting tools. For example, Twitter attracted many social science researchers due to its high popularity, constrained format of thought expression, and the ability to react actual trends. However, unstructured data from microblogs often suffer from the lack of representativeness due to the tremendous amount of noise. Such noise is often introduced by the activity of organizational and fake user ac-counts that may not be useful in many application domains. Aiming to tackle the information filtering problem, in this paper, we classify Twitter accounts into three categories: “Personal”, “Organization”, and “Personage”. Specifically, we utilize various text-based data representation approaches to extract features for our proposed microblog account type prediction framework “POP-MAP”. To study the problem at a cross-language level, we harvested and learned from a multi-lingual Twitter dataset, which allows us to achieve better classification performance, as compared to various state-of-the-art baselines.
Characterizing the Performance and Resilience of HCI Clusters with the TPCx-HCI Benchmark

We use the newly-released TPCx-HCI benchmark to characterize the performance and resilience properties of Hyper-Converged Infrastructure clusters. We demonstrate that good performance on an HCI cluster requires delivering all properties of high IOPS, low latencies, low CPU overhead, and uniform access to data from all Nodes. We show that unless the cluster can quickly and efficiently rebalance the VMs after a change in the workload, performance will be severely impacted. We use the data accessibility test of TPCx-HCI to show how performance is impacted by rebuilding traffic after a Node goes down, and how long it takes for the rebuilding to finish.