Reviewerly/exHarmony huggingface dataset:
LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction

LOGISMOS (Layered Optimal Graph Image Segmentation for Multiple Objects and Surfaces) is a general approach for optimally segmenting multiple n-D surfaces that mutually interact within individual and/or between object. JEI (Just-Enough Interaction) extension enables the users to intuitively and efficiently guide LOGISMOS to achieve clinically acceptable segmentation that still maintains global optimality, embedded shape, and anatomy knowledge. This chapter is aimed at both the conceptual designers of quantitative medical image analysis methods and developers adopting LOGISMOS-JEI to perform new segmentation tasks. The basic principles and essential techniques of LOGISMOS-JEI are presented. Using three clinical segmentation problems, the process of designing LOGISMOS-JEI applications is described with a focus on choosing proper techniques at all stages of the analysis pipeline. The capability of JEI is demonstrated by reporting the achieved segmentation accuracy and quantifying the levels of human effort involved.
Secure Deep Learning Engineering: A Road Towards Quality Assurance of Intelligent Systems

Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics. Deep neural networks are the key driving force behind its recent success, but still seem to be a magic black box lacking interpretability and understanding. This brings up many open safety and security issues with enormous and urgent demands on rigorous methodologies and engineering practice for quality enhancement. A plethora of studies have shown that state-of-the-art DL systems suffer from defects and vulnerabilities that can lead to severe loss and tragedies, especially when applied to real-world safety-critical applications. In this paper, we perform a large-scale study and construct a paper repository of 223 relevant works to the quality assurance, security, and interpretation of deep learning. Based on this, we, from a software quality assurance perspective, pinpoint challenges and future opportunities to facilitate drawing the attention of the software engineering community towards addressing the pressing industrial demand of secure intelligent systems.
RSA based encryption approach for preserving confidentiality of big data

Sensitive Health Information (SHI) is a developing patient-centric model of medical data exchange, which is frequently outsourced to be stored at third party servers. Though, there have been various privacy issues as SHI could be disclosed to the unauthorised and third parties. This is a promising method to encrypt the SHI before outsourcing to assure the patients’ control over access to their own SHI. However, challenges like scalability in key management and flexible access have remained the most significant issues toward achieving fine-grained, cryptographically data access control. In this paper, the authors proposed a novel patient-centric system model for access control to SHIs stored in semi-honest servers. For fine-grained and scalable access control for SHIs, authors have proposed an encryption technique which is an improvement over RSA techniques to encrypt every patient’s SHI file. To different from previous works in secure data transmission, the authors focus on the data owner and divide users into several domains in SHI, which greatly decrease the key management complexity for data owners and users. Comprehensive analytical and experimental results are presented which reflect the efficiency of the proposed approach.
Towards Optimal CNN Descriptors for Large-Scale Image Retrieval

Instance-level image retrieval is a long-standing and challenging problem in multimedia. Recently, fine-tuning Convolutional Neural Networks (CNNs) has become a promising direction, and a number of successful strategies based on global CNN descriptors have been proposed. However, it is difficult to make direct comparisons and draw conclusions due to different settings and/or datasets. The goal of this paper is two-fold. Firstly, we present a unified implementation of modern global-CNN-based retrieval systems, break such a system into six major components, and investigate each part individually as well as globally when considering different configurations. We conduct a systematic series of experiments on a component-by-component basis and find an optimal solution in designing such a system. Secondly, we introduce a novel joint loss function with learnable parameter for fine-tuning for retrieval tasks and show, with extensive experiments, significant improvement over previous works. On the new and challenging large-scale Google-Landmarks-Dataset, we set a baseline for future research and comparisons, while on traditional retrieval benchmarks such as Oxford5k and Paris6k, as well as their recent revised versions ROxford5k and RParis6k, we achieve state-of-the-art performance under all three (Easy, Medium, and Hard) evaluation protocals by a large margin compared to competing methods.
Adding virtual reality to the university self-access language centre

Immersing students in communicative situations facilitates learning, engages emotions, and fosters socio-cognitive conflicts leading learners to develop abilities (Dewaele 2010; Bourguignon 2006; Privas-Bréauté 2017). In this context, the UFR Lansad – languages for the specialists of other disciplines – in charge of language teaching policy for non-specialist students at the University of Lorraine (France) opened a virtual reality (VR) space in which experimentations are carried out. Researchers intend to study how VR might support second language development and contribute to enhance students' cultural awareness. To do so, VR workshops are offered, VR sessions with student teachers implemented and an international collaboration experimentation in English for Specific Purposes (ESP) is under way. Although learners' reactions and feedback are very positive, the first implementation of the new VR space leads us to understand that VR and its pedagogical uses might suffer from some flaws.
Determining the most representative image on a Web page

We investigate how to determine the most representative image on a Web page. This problem has not been thoroughly investigated and, up to today, only expert-based algorithms have been proposed in the literature. We attempt to improve the performance of known algorithms with the use of Support Vector Machines (SVM). Besides, our algorithm distinguishes itself from existing literature with the introduction of novel image features, including previously unused meta-data protocols. Also, we design and attempt a less-restrictive ranking methodology in the image preprocessing stage of our algorithm. We find that the application of the SVM framework with our improved classification methodology increases the F1 score from 27.2% to 38.5%, as compared to a state-of-the-art method. Introducing novel image features and applying backward feature selection, we find that the F1 score rises to 40.0%. Lastly, we use a class-weighted SVM in order to resolve the imbalance in number of representative images. This final modification improves the classification performance of our algorithm even further to 43.9%, outperforming our benchmark algorithms, including those of Facebook and Google. Suggested beneficiaries are the search engine community, image retrieval community, including the commercial sector due to superior performance.
Intelligent Voice Bots for Digital Banking

Current digital payment solutions are passwords or PIN-based and are complex and time-consuming process with 5+ steps including downloading an app, thereby impacting bandwidth and cost. Moreover, these are susceptible to phishing/vishing/theft/hacking. Also, visually impaired/specially abled are not able to access current payment processes. In this paper, "Voice Pay" solution has been developed which aims at making banking personal and possible for anyone using the most secured authentication which is "voice". Without any additional app, it provides a solution that allows everyone (including visually impaired/specially abled) to perform banking operations with ease in the most secured way. It allows, from cumbersome 5+ step process, to an extremely cost-effective few seconds process making it simple, time-saving, and cost-effective process. It uses available ML and AI/NLP solutions of intelligent virtual assistants and is less vulnerable to breaches than any credential. Sample use case has been undertaken.
Towards Latent Attribute Discovery From Triplet Similarities

This paper addresses the task of learning latent attributes from triplet similarity comparisons. Consider, for instance, the three shoes in Fig. 1(a). They can be compared according to color, comfort, size, or shape resulting in different rankings. Most approaches for embedding learning either make a simplifying assumption - that all inputs are comparable under a single criterion, or require expensive attribute supervision. We introduce Latent Similarity Networks (LSNs): a simple and effective technique to discover the underlying latent notions of similarity in data without any explicit attribute supervision. LSNs can be trained with standard triplet supervision and learn several latent embeddings that can be used to compare images under multiple notions of similarity. LSNs achieve state-of-the-art performance on UT-Zappos-50k Shoes and Celeb-A Faces datasets and also demonstrate the ability to uncover meaningful latent attributes.
Cross-Fiber Spatial-Temporal Co-enhanced Networks for Video Action Recognition

The 3D convolutional neural networks recently have been applied to explore spatial-temporal content for video action recognition. However, they either suffer from high computational cost by spatial-temporal feature extraction or ignore the correlation between appearance and motion. In this work, we propose a novel Cross-Fiber Spatial-Temporal Co-enhanced (CFST) architecture aiming to reduce the number of parameters tremendously while achieve accurate recognition of actions. We slice the complex 3D convolutional network into a group of lightweight fibers that run through the whole network. Crossing separated fibers, we introduce the Cross-Fiber Recalibration unit which shares extracted features from each fiber and measures the interaction between fibers to emphasize informative ones. Within each fiber, the Spatial-Temporal Co-enhanced unit is put forward to co-enhance the learning of spatial and temporal features, leading to more discriminative spatial-temporal representation. An end-to-end deep network, CFST-Net, is also presented based on the proposed CFST architecture for video action recognition. Extensive experimental results show that our CFST-Net significantly boosts the performance of existing convolution networks and achieves state-of-the-art accuracy on three challenging benchmarks, i.e., UCF-101, HMDB-51 and Kinetics-400, with much fewer parameters and FLOPs.
User-Aware Folk Popularity Rank

In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user popularity. A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity. However, the popularity of an image/video is strongly affected by who uploaded it. Therefore, we develop an algorithm that can incorporate user popularity and users' tag usage tendency into the FP-Rank algorithm. The experimental results using 60,000 training images with their accompanying tags and 1,000 test data, which were actually uploaded to a real social network service (SNS), show that, in ten days, our proposed algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This technology would be critical to individual users and companies/brands who want to promote themselves in SNSs.
Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks

Video streaming is one of the top traffic contributors in the Internet and a frequent research subject. It is expected that streaming traffic will grow 4-fold for video globally and 9-fold for mobile video between 2017 and 2022. In this paper, we present an automatized measurement framework for evaluating video streaming QoE in operational broadband networks, using headless streaming with a Docker-based client, and a server-side implementation allowing for the use of multiple video players and adaptation algorithms. Our framework allows for integration with the acsMONROE testbed and Bitmovin Analytics, which bring on the possibility to conduct large-scale measurements in different networks, including mobility scenarios, and monitor different parameters in the application, transport, network, and physical layers in real-time.
Interactive Soft Toys for Infants and Toddlers - Design Recommendations for Age-appropriate Play

Very young children (below three) are characterized by sensory-motor exploration and pre-cognitive development. There is little work on interactive toys for this age group. This raises the question of what interactions are developmentally appropriate at this age. We here propose recommendations for designing meaningful interactive toys, gained from designing an interactive soft (textile) book prototype, testing it with children and discussion of observations with parents, as well as three expert interviews. Recommendations concern what types of interactions to implement (transparent and one-step), inviting full-body activity and exploration with all senses, what kind of effects to generate (appropriate for children's abilities, being predictable, with clear relation to real-world experiences, but also accounting for the needs of caregivers), and the importance of allowing for shared experience.
512KiB RAM Is Enough! Live Camera Face Recognition DNN on MCU

Small factor and ultra-low power devices are becoming more and more smart and capable even for deep learning network inference. And as the devices are "small", the challenge is becoming tougher. This paper covers full development and deployment pipeline of Face Recognition with a live camera - from model training and quantization to porting to RISC-V MCU with 512 kilobytes of internal RAM. Authors provide GreenWaves GAP8 SoC overview and the approaches for DNN model optimization and inference in the extreme environment. As the project outcome, authors were able to run Face Detection and Recognition with live QVGA camera and display preview on a battery-powered board.
Online Camera Pose Optimization for the Surround-view System

Surround-view system is an important information medium for drivers to monitor the driving environment. A typical surround-view system consists of four to six fish-eye cameras arranged around the vehicle. From these camera inputs, a top-down image of the ground around the vehicle, namely the surround-view image can be generated with well calibrated camera poses. Although existing surround-view system solutions can estimate camera poses accurately in off-line environment, how to correct the camera poses' change in online environment is still an open issue. In this paper, we propose a camera pose optimization method for surround-view system in online environment. Our method consists of two models: Ground Model and Ground-Camera Model, both of which correct the camera poses by minimizing photometric errors between ground projections of adjacent cameras. Experiments show that our method can effectively correct the geometric misalignment of the surround-view image caused by camera poses' change. Since our method is highly automated with low requirement of calibration site and manual operation, it has a wide range of applications and is convenient for the end-users. To make the results reproducible, the source code is publicly available at https://cslinzhang.github.io/CamPoseOpt/.
Design and Implementation of a Hand Movement Controlled Robotic Vehicle with Wireless Live Streaming Feature

This paper illustrates the design and implementation of a robotic vehicle that can be controlled from all directions by using the hand movement with a wireless camera that is installed at the top of the vehicle to broadcast wireless live streaming to the user end. Therefore, it removes the hassle of gesture recognition and image processing technique or even the use of switches or joysticks to control the movement of a robot in different directions and provides a wireless surveillance facility to the user. There are mainly three parts of this propose system; the transmitter, the receiver and the live streaming section. The transmitter section detects the movement of the hand in x and y axis by using an accelerometer sensor, processed by Arduino Uno, and transmits those signals to the receiver section by using RF module. The receiver section receives those signals via RF module, decoded by the Arduino Uno, and the motor driver, which is connected to the Arduino Uno and moves the robotic vehicle in all directions according to the decoded signals. Finally, in order to perform live streaming operation - a common network (Wi-Fi), a laptop, Raspberry pi, and two software, namely Virtual Network Computing (VNC) and Foundation Internet Nouvelle Generation (FING) are used to ensure the surveillance feature for the user.
Adversarial Examples in Deep Neural Networks: An Overview

Deep learning architectures are vulnerable to adversarial perturbations. They are added to the input and alter drastically the output of deep networks. These instances are called adversarial examples. They are observed in various learning tasks from supervised learning to unsupervised and reinforcement learning. In this chapter, we review some of the most important highlights in theory and practice of adversarial examples. The focus is on designing adversarial attacks, theoretical investigation into the nature of adversarial examples, and establishing defenses against adversarial attacks. A common thread in the design of adversarial attacks is the perturbation analysisPerturbation analysis of learning algorithms. Many existing algorithms rely implicitly on perturbation analysisPerturbation analysis for generating adversarial examples. The summary of most powerful attacks are presented in this light. We overview various theories behind the existence of adversarial examples as well as theories that consider the relation between the generalization error and adversarial robustness. Finally, various defenses against adversarial examplesAdversarial examples are also discussed.
Avoiding the Hay for the Needle in the Stack: Online Rule Pruning in Rare Events Detection

In certain machine learning application domains such as those that arise in Industry 4.0 settings that involve rare events detection and prediction, classifiers based on Rule Mining often have an advantage over other algorithms due to their design that allows them to deal with highly skewed class distributions. We describe R4RE, an algorithm that mines all quantitative association rules from large datasets that meet certain user-defined criteria of interestingness that is not limited to the classical support/confidence framework. When dealing with very small classes, the possibility arises that rules with very small support may be valid in the training set that do not hold in the test set. To avoid generating such rules, we implement the approach found in the IREP and other systems where rules may be pruned immediately after discovery. Results from real world factory data show the efficiency of our approach for predictive maintenance purposes.
Side-Channel Attack on the A5/1 Stream Cipher

In this paper we present cryptanalysis of the A5/1 stream cipher used in GSM mobile phones. Our attack is based on power analysis where we assume that the power consumption while clocking 3 LFSRs is different than when clocking 2 LFSRs. We demonstrate a simple power analysis (SPA) attack and discuss existing differential power analysis (DPA). We present the attack for recovering secret key based on the information on clocking bits of LFSRs that was deduced from power analysis. The attack has a 100% success rate, requires minimal storage and it does not requires any single bit of a keystream. An average time complexity of our attack based on SPA is around 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">33</sup> where the computation unit is a resolution of system of linear equations over the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Recovering the secret key using information from the DPA has a constant complexity.
Parameter selection algorithm of DBSCAN based on K‐means two classification algorithm

Clustering algorithm is one of the most important algorithms in unsupervised learning. For density-based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborhood radius and minimum number is the key to get the best clustering results. Aiming at the problems of traditional DBSCAN algorithm, such as the neighborhood radius and the minimum number of points, this article puts forward two classifications based on K-means algorithm, and gets two clustering centers. Where calculated between two data points and the cluster center-to -center distance, clustering, distance, statistics in a distance of data points within the scope of the search, the number of data points corresponding to the maximum distance value, and thus the parameters for the DBSCAN algorithm to estimate and selection of initial radius of neighborhood with the minimum number of clustering start critical value. When the parameters are iterated and optimized continuously, the data are divided into clusters, and the most suitable neighborhood radius and the minimum point number are obtained. The experimental data analysis show that the improved algorithm reduces the human factors in the traditional algorithm and improves the efficiency, so as to get the accurate clustering results.
Pedestrian Attribute Recognition via Hierarchical Multi-task Learning and Relationship Attention

Pedestrian Attribute Recognition (PAR) is an important task in surveillance video analysis. In this paper, we propose a novel end-to-end hierarchical deep learning approach to PAR. The proposed network introduces semantic segmentation into PAR and formulates it as a multi-task learning problem, which brings in pixel-level supervision in feature learning for attribute localization. According to the spatial properties of local and global attributes, we present a two stage learning mechanism to decouple coarse attribute localization and fine attribute recognition into successive phases within a single model, which strengthens feature learning. Besides, we design an attribute relationship attention module to efficiently capture and emphasize the latent relations among different attributes, further enhancing the discriminative power of the feature. Extensive experiments are conducted and very competitive results are reached on the RAP and PETA databases, indicating the effectiveness and superiority of the proposed approach.