Reviewerly/exHarmony huggingface dataset:
Interactive Image Segmentation Based on Label Pair Diffusion

This article explores the relationships between image element pairs and label pairs and extends label diffusion to label pair diffusion for the interactive image segmentation task. Compared with label diffusion, more accurate relationships between unlabeled and labeled data can be captured on a tensor product graph (TPG) by using higher order information, and more complex interactions among image elements and finer relationships between image element pairs and label pairs are explored in label pair diffusion (LPD) process. We first establish a prior label estimation framework to measure the label pair prior probability. Then, a probability learning process on TPG is designed to smooth the label prior. The learning process is equivalent to an iterative LPD process on the original graph, which makes the proposed algorithm maintain computational efficiency. Finally, the unary label probabilities can be obtained by a total-probability-theorem-based conversion from the binary relationships. Experiments on popular segmentation data sets demonstrate the superior performance of the proposed method.
A probative value for authentication use case blockchain

The Fintech industry has facilitated the development of companies using blockchain technology. The use of this technology inside banking system and industry opens the route to several questions regarding the business activity, legal environment, and insurance devices. In this paper, considering the creation of small companies interested to develop their business with a public blockchain, we analyse from different aspects why a company (in banking or insurance system, and industry) decides that a blockchain protocol is more legitimate than another one for the business which it wants to develop looking at the legal (in case of dispute) points of view. We associate with each blockchain a probative value which permits to assure in case of dispute that a transaction has been really done. We illustrate our proposal using 13 blockchains providing in that case a ranking between these blockchains for their use in business environment. We associate with this probative value some main characteristics of any blockchain as market capitalization and log-return volatilities that the investors need to also take into account with the new probative value for their managerial strategy.
Waving Gesture Analysis for User Authentication in the Mobile Environment

The increasing popularity of wearable devices has brought great convenience to human life and business. As wearable devices have become widely used personal computing platforms, more and more private information gets accessed by them, which stresses an urgent need for feasible and reliable authentication mechanisms in the current mobile computing environment. However, traditional memory- based authentication methods like PINs have been proven easy to crack or steal. Based on the fact that hand-waving patterns vary among different users, we propose a novel hand-waving- based unlocking system using smartwatches, which consists of data acquisition, data preprocessing, feature extraction, and authentication modules. Furthermore, we established a 150-person-time hand-waving dataset with a smartwatch, and conducted a systematic performance evaluation, achieving an equal error rate of 4.27 percent in the zero-effort attacking scenario and 14.46 percent in the imitation-attack scenarios. Additional experiments on usability to operation length and sensitivity to sampling frequency are offered to explore the applicability and effectiveness.
Debiased offline evaluation of recommender systems

Offline evaluation of recommender systems mostly relies on historical data, which is often biased by many confounders. In such data, user-item interactions are Missing Not At Random (MNAR). Measures of recommender system performance on MNAR test data are unlikely to be reliable indicators of real-world performance unless something is done to mitigate the bias. One way that researchers try to obtain less biased offline evaluation is by designing new supposedly unbiased performance estimators for use on MNAR test data. We investigate an alternative solution, a sampling approach. The general idea is to use a sampling strategy on MNAR data to generate an intervened test set with less bias --- one in which interactions are Missing At Random (MAR) or, at least, one that is more MAR-like. An example of this is SKEW, a sampling strategy that aims to adjust for the confounding effect that an item's popularity has on its likelihood of being observed.
Efficient Neural Network Space with Genetic Search

We present a novel neural architecture search space and its search strategy with an evolutionary algorithm. It aims to find a set of inverted bottleneck structure blocks, which takes a low-dimensional input representation followed by a compressing layer. Primitive operation layers constitute flexible inverted bottleneck blocks and can be assembled in evolutionary operation. Because the bottleneck structure confines the search space, the proposed evolutionary search algorithm can easily find a competitive neural network despite its small population size. During the search process, we designed to evaluate a model to avoid local minimums: such implementation helped the algorithm to discard local minimums and find better models. We conducted experiments on image classification of Fashion-MNIST, and we discovered an efficiently optimized neural network achieving 6.76 for an error rate with 356 K parameters.
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution

The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE – a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient Classification Pipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.
Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle (Extended Abstract)

This work covers the robotic drone integration of a re-identification system for Friesian Cattle. We have built a computationally-enhanced M100 UAV platform with an on-board deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian cattle in freely moving herds. For autonomous drone-based identification we describe an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream Convolutional Neural Network (CNN) delivering exploratory agency and an InceptionV3-based biometric Long-term Recurrent Convoluational Network (LRCN) for individual animal identification. We evaluate the performance of components offline, and also online via real-world field tests of autonomous low-altitude flight in a farm environment. The presented proof-of-concept system is a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments and inside farms for tag-less AI support in farming and ecology. The work is published in full in IROS 2019 [4].
Group Signatures with Decentralized Tracing

Group signature is a useful cryptographic primitive that allows a message to be signed by a user on behalf of a group which is managed by some trusted authority, namely the group manager. However, group signature schemes typically place a disturbingly high level of trust on the group manager, which has become a major deployment issue in cyber applications where there is no centralized trust management. In this paper, we investigate mechanisms that aim to achieve a balance between anonymity and accountability in group signatures by decentralizing the operation of tracing the signer. We propose a practical group signature scheme with decentralized tracing. When comparing with a similar result by Benjumea et al. (FC 2008), our proposal has the advantage of a shorter signature size.
Authenticated and auditable data sharing via smart contract

Our main use case features multiple companies that iteratively optimize on the architectural properties of aircraft components in a decentralized manner. In each optimization step of the so-called multi-disciplinary optimization (MDO) process, sensitive data is exchanged between organizations, and we require auditability and traceability of actions taken to assure compliance with signed legal agreements.
VTKEL

To understand the content of a document containing both text and pictures, an artificial agent needs to jointly recognize the entities shown in the pictures and mentioned in the text, and to link them to its background knowledge. This is a complex task, that we call Visual-Textual-Knowledge Entity Linking (VTKEL), which aims at linking visual and textual entity mentions to the corresponding entity (or a newly created one) of the agent knowledge base. Solving the VTKEL task opens a wide range of opportunities for improving semantic visual interpretation. For instance, given the effectiveness and robustness of state-of-the-art NLP technologies in entity linking, by automatically linking visual and textual mentions of the same entities with the ontology, we can obtain a huge amount of automatically annotated images with detailed categories. In this paper, we propose the VTKEL dataset, consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The VTKEL dataset can be used for training and evaluating algorithms for visual-textual-knowledge entity linking.
Post quantum proxy signature scheme based on the multivariate public key cryptographic signature

Proxy signature is a very useful technique which allows the original signer to delegate the signing capability to a proxy signer to perform the signing operation. It finds wide applications especially in the distributed environment where the entities such as the wireless sensors are short of computational power and needed to be convinced to the authenticity of the server. Due to less proxy signature schemes in the post-quantum cryptography aspect, in this article, we investigate the proxy signature in the post-quantum setting so that it can resist against the potential attacks from the quantum adversaries. A general multivariate public key cryptographic proxy scheme based on a multivariate public key cryptographic signature scheme is proposed, and a heuristic security proof is given for our general construction. We show that the construction can reach Existential Unforgeability under an Adaptive Chosen Message Attack with Proxy Key Exposure assuming that the underlying signature is Existential Unforgeability under an Adaptive Chosen Message Attack. We then use our general scheme to construct practical proxy signature schemes for three well-known and promising multivariate public key cryptographic signature schemes. We implement our schemes and compare with several previous constructions to show our efficiency advantage, which further indicates the potential application prospect in the distributed network environment.
ID-Based User-Centric Data Usage Auditing Scheme for Distributed Environments

Recent years have witnessed the trend of increasingly relying on remote and distributed infrastructures, mainly owned and managed by third parties. This increased the number of reported incidents of secu- rity breaches compromising users’ personal data, where involved entities may massively collect and process massive amounts of such data. To- wards these challenges, this paper combines hierarchical Identity Based Cryptographic (IBC) mechanisms with emerging blockchain technologies and introduces a blockchain-based data usage auditing architecture en- suring availability and accountability in a personal data-preserving fash- ion. The proposed approach relies on smart auditable contracts deployed in blockchain infrastructures. Thus, it offers transparent and controlled data access, sharing and processing, so that unauthorized entities can- not process data without data subjects’ consent. Moreover, thanks to the usage of hierarchical ID-based encryption and signature schemes, the proposed solution protects and ensures the confidentiality of users’ personal data shared with multiple data controllers and processors. It also provides auditing capacities with tamper-proof evidences for data usage compliance, supported by the intrinsic properties of the blockchain technology.
Mitigating Cyber Supply Chain Risks in Cyber Physical Systems Organizational Landscape

Cyber supply chain (CSC) provide an organization with the ability to align its business processes, information flows and data structures with other organization. However, the increase interdependencies have brought about inherent, threats, risks, attacks and vulnerabilities that adversaries maybe able to exploit when not properly mitigated. Additionally, every cyberattack on each organization increases the probability of the risk cascading to others. The CSC risk has increased exponentially due to uncertainties surrounding cyberattacks and the cyber threat landscape. Recent CSC threats have been disruptive and impacting on the smooth flow of delivery of products and services. CSC risk has been observed as one of the areas that impact greatly and causes budget overruns. The aim of this paper is to mitigate CSC risks in an organizational landscape. In particular, the paper identifies supply inbound and outbound chain threat landscape using a risk breakdown structure. Further, weassess the risk to gather cyber threat intelligence. Furthermore, we use the probability distribution method to determine the CSC risks and analyze the risk probabilities and likelihood of risk cascading impact. Our results show that CSC risk can be neutralized using probability distribution methods to detect and mitigate the risks and their impact levels.
Deep Clustering for Domain Adaptation

We address the heterogeneous domain adaptation task: adapting a classifier trained on data from one domain to operate on another domain that also has a different label space. We consider two settings that both exhibit label scarcity of some form—one where only unlabelled data is available, and another where a small volume of labelled data is available in addition to the unlabelled data. Our method is based on two specialisations of a recently proposed approach for deep clustering. It is shown that our approach noticeably outperforms other methods based on deep clustering in both the fully unsupervised and the semi-supervised settings.
Nasil: Neural Architecture Search with Imitation Learning

Automated machine learning (AML) refers to a class of techniques that, given a problem, can find an optimal set of model architectures, properties, and parameters. In recent years, AML has shown great success in finding neural network structures that are as good as the hand-designed structures and even better. AML is usually a time consuming process that needs to search a big search space. All available AML solutions start the search from scratch. Here we introduce neural architecture search with imitation learning (NASIL) method that starts the search by learning from hand designed structures by experts. NASIL is an actor-critic structure that uses hindsight experience replay(HER) and starts by training on these hand-designed structures stored in memory. We tested NASIL found structures on publicly available Google speech command dataset as well as in-house dataset. Our results show that NASIL is able to find a better structure than handdesigned structures in terms of accuracy and number of parameters. More importantly it is faster that similar approaches.
Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer

Goal-oriented dialogue systems are now widely adopted in industry, where practical aspects of using them becomes of key importance. As such, it is expected from such systems to fit into a rapid prototyping cycle for new products and domains. For data-driven dialogue systems (especially those based on deep learning) that amounts to maintaining production-level performance having been provided with a few `seed' dialogue examples, normally referred to as data efficiency.With extremely data-dependent deep learning methods, the most promising way to achieve practical data efficiency is transfer learning-i.e., leveraging a greater, highly represented data source for training a base model, then fine-tuning it to available in-domain data.In this paper, we present a hybrid generative-retrieval model that can be trained using transfer learning. By using GPT-2 as the base model and fine-tuning it to the multidomain MetaLWOz dataset, we obtain a robust dialogue model able to perform both response generation and ranking <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Combining both, it outperforms several competitive generative-only and retrieval-only baselines, measured by language modeling quality on MetaLWOz as well as in goal- oriented metrics (Intent/Slot Fl-scores) on the MultiWoz corpus.
The Implementation of a Practical Agricultural Big Data System

Agricultural big data is growing rapidly and it faces many known and unknown obstacles. This paper focuses on the need for managing farms effectively and making decisions accurately. Using agricultural IoT technology, big data technology and machine learning algorithms, the big data application system is established. Including data collection, data storage, data analysis and visualization management, we present a complete scheme. Through the agricultural big data system, the production cost is reduced and the production efficiency is improved.
Composing Indonesian Paragraph for Biography Domain using Extracive Summarization

A main problem for Wikipedia and other wiki-based websites is the need of human resource to create new articles and update existing articles. Furthermore, the biography domain is one of the most active domains as each person's article need to be updated as time goes by. In this paper, we investigate an approach to update Indonesian Wikipedia articles, specifically in person biography domain, automatically by practicing extractive summarization of information drawn from online news portal. We use news articles about a person to compose candidate paragraphs for updating the person's Wikipedia article. The candidate paragraphs would be filtered to reduce the possibility of redundancy and given a section recommendation based on the existing sections in the person's current Wikipedia article. Given a person's name, we show that our model can find and extract relevant information about the person as reflected by the ROUGE score for summarization and human evaluation for the overall result.
A Study on the Transferability of Adversarial Attacks in Sound Event Classification

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks.
Pixel-Level Self-Paced Learning For Super-Resolution

Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields. However, with these networks being constructed deeper and deeper, they also cost much longer time for training, which may guide the learners to local optimization. To tackle this problem, this paper designs a training strategy named Pixel-level Self-Paced Learning (PSPL) to accelerate the convergence velocity of SISR models. PSPL imitating self-paced learning gives each pixel in the predicted SR image and its corresponding pixel in ground truth an attention weight, to guide the model to a better region in parameter space. Extensive experiments proved that PSPL could speed up the training of SISR models, and prompt several existing models to obtain new better results. Furthermore, the source code is available at https://github.com/Elin24/PSPL.
Autonomous Step Climbing Strategy Using a Wheelchair and Care Robot

This report describes a cooperative step climbing strategy using an electric wheelchair and autonomous robot. The robot, which was developed by our research group, has a wheeled travel mechanism and dual manipulators. The wheelchair is a commercially available model modified with added sensors, circuits, and batteries. When the wheelchair and robot encounter a step, the robot grasps the wheelchair and they help each other to ascend the step. In the step climbing process, the wheelchair front wheels are lifted using the difference between the wheelchair and robot velocities, and the front wheels are placed on the step. To make the rear wheels of the wheelchair climb the step, the robot upper arms push against the back of the wheelchair, which is like the motion of man pushing a wheelchair up the step. Similarly, the robot front and rear wheels climb the step using assistance from the wheelchair. We developed an automatic control system that realizes the cooperative step climbing of the wheelchair and robot and also simplifies the operation of both vehicles. An experiment was conducted to demonstrate that the wheelchair and robot can successfully maneuver up a step.