Reviewerly/exHarmony huggingface dataset:
Learning high-order structural and attribute information by knowledge graph attention networks for enhancing knowledge graph embedding

The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding space. Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction. However, we observe that: (1) existing methods simply take direct relations between entities into consideration and fails to express high-order structural relationships between entities; (2) these methods simply leverage relation triples of Knowledge Graphs while ignoring a large number of attribute triples that encode rich semantic information. To overcome these limitations, this paper proposes a novel knowledge graph embedding method, named ( KANE ), which is inspired by the recent developments in graph convolutional networks (GCN). KANE can capture both high-order structural and attribute information of Knowledge Graphs in an efficient, explicit and unified manner under the graph convolutional networks framework. Empirical results on three datasets show that KANE significantly outperforms seven state-of-the-art methods. Further analysis verify the efficiency of our method and the benefits brought by the attention mechanism.
Minimizing Trust in Hardware Wallets with Two Factor Signatures

We introduce the notion of two-factor signatures (2FS), a generalization of a two-out-of-two threshold signature scheme in which one of the parties is a hardware token which can store a high-entropy secret, and the other party is a human who knows a low-entropy password. The security (unforgeability) property of 2FS requires that an external adversary corrupting either party (the token or the computer the human is using) cannot forge a signature. This primitive is useful in contexts like hardware cryptocurrency wallets in which a signature conveys the authorization of a transaction. By the above security property, a hardware wallet implementing a two-factor signature scheme is secure against attacks mounted by a malicious hardware vendor; in contrast, all currently used wallet systems break under such an attack (and as such are not secure under our definition). We construct efficient provably-secure 2FS schemes which produce either Schnorr signature (assuming the DLOG assumption), or EC-DSA signatures (assuming security of EC-DSA and the CDH assumption) in the Random Oracle Model, and evaluate the performance of implementations of them. Our EC-DSA based 2FS scheme can directly replace currently used hardware wallets for Bitcoin and other major cryptocurrencies to enable security against malicious hardware vendors.
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation

Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization method, applied for the first time to 3D Fully Convolutional Neural Networks, enabling 16x model compression while maintaining performance on par with full precision models. We extensively evaluate 3DQ on two datasets for the challenging task of whole brain segmentation. Additionally, we showcase the ability of our method to generalize on two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety of baselines, the proposed method is capable of compressing large 3D models to a few MBytes, alleviating the storage needs in space-critical applications.
Learning colon centreline from optical colonoscopy, a new way to generate a map of the internal colon surface

Optical colonoscopy is known as a gold standard screening method in detecting and removing cancerous polyps. During this procedure, some polyps may be undetected due to their positions, not being covered by the camera or missed by the surgeon. In this Letter, the authors introduce a novel convolutional neural network (ConvNet) algorithm to map the internal colon surface to a 2D map (visibility map), which can be used to increase the awareness of clinicians about areas they might miss. This was achieved by leveraging a colonoscopy simulator to generate a dataset consisting of colonoscopy video frames and their corresponding colon centreline (CCL) points in 3D camera coordinates. A pair of video frames were used as input to a ConvNet, whereas the output was a point on the CCL and its direction vector. By knowing CCL for each frame and roughly modelling the colon as a cylinder, frames could be unrolled to build a visibility map. They validated their results using both simulated and real colonoscopy frames. Their results showed that using consecutive simulated frames to learn the CCL can be generalised to real colonoscopy video frames to generate a visibility map.
Image Augmentation with Neural Style Transfer

The amount of training data is of crucial importance for the performance of machine learning, and especially deep learning models. It is one of the most important factors that determine whether the developed model is effective or not. When the quantity of training data for a computer vision problem is insufficient, various data augmentation techniques are used to artificially extend the training dataset with samples that retain the natural distribution of the original data. This paper proposes and evaluates a deep learning model that will be used for image augmentation. A complex deep neural network makes use of transfer learning in order to learn the characteristics of the content and style of the training images, create random style embeddings via learned multivariate normal distribution, and ultimately generate images to extend the original dataset. The model is trained on two datasets which are frequently used in computer vision: ImageNet and Painter by Numbers (PBN). Afterwards, the model is used to generate new images from the CIFAR-100 and Tiny-ImageNet-200 datasets. The performance of the augmentation model is evaluated by a separate convolutional neural network. The evaluation model is trained on the combined dataset, consisting of both, the original and augmented images, and then compared to the performance of the same model trained on the original datasets.
A Naive Bayes approach to fraud prediction in loan default

Abstract The essence of granting loans to individuals and corporate beneficiaries is to boost the economy while the lenders make profit from the interest that accrues to the lending. However, due to non-compliance to basic rules, fraud is prevalent in credit administration and traditional methods of detecting fraud have failed. Furthermore, they are time-consuming and less accurate. This work uses a supervised machine learning approach, specifically the Naïve Bayes to predict fraudulent practices in loan administration based on training and testing of labeled dataset. Previous works either predict credit worthiness or detect loan fraud but not predicting fraud in credit default. The approach employed in this work yielded 78 % accuracy.
Using cartoons agents and 3D visualizations based on HTML5 for improving learning in crystal structures in engineers

Abstract This paper presents an educational digital material on “solid crystal structures,” in which cartoons agents direct undergraduate students on a learning cycle of understanding concepts from the submicroscopic level and correlate with the corresponding macroscopic‐related properties. Specifically, undergraduate students are given the opportunity to study basic crystal structures starting from structural units, composing the unit cells in a three‐dimensional (3D) pattern that are characterized and related with intermolecular forces within them, and finally leading to basic crystalline solids associated with the macroscopic behavior of solid materials expressed by physical and mechanical properties such as hardness, electrical conductivity, etc. The design of the digital material was focused on the cognitive benefits that students gained after viewing 3D visualizations for which students were able to manipulate the rotation or size of them, to enhance their conceptual understanding addressed to chemistry and engineering, accompanied by cartoons agents. Digital material was constructed to be accessible from and compatible with any web browser without any third‐party plugin. A research using a properly designed questionnaire was conducted in the School of Chemical Engineering in Greece where the evaluation indicated that students preferred using this web material due to its simplicity and their active involvement by self‐controlling the pace and the way of learning.
A Robust Q-Learning and Differential Evolution Based Policy Framework for Key Frame Extraction

With the recent development in multimedia technologies, in rapid conjunction with the increase of the volume of digital video data through internet and web technologies. For this purpose solely, content based video retrieval (CBVR) has become a wide and vast area of research throughout the last decade. The objective of this thesis is to present applications for temporal video frame analysis based on performance evaluation of key frames and video sequence from the extracted key frames retrieval based on different mathematical models. In this work, through performance analysis, we extracted the key frames from a video into its constituent units. This is achieved by identifying transitions between adjacent temporal features. The proposed algorithm aims to extract the key frames based on the validation measures and cross mutation function through the modified differential evaluation algorithm. Given the size of the vector containing image pixels, it can be modeled by a parameter based cross evaluation function of the parent vector. The proposed system, designed for extracting key frames, has led to reliable algorithm achieving high performance for object re-identification. In addition, the high computational time allows for key frame analysis in real time. In our research, we opted for a global method based on local optimization. The proposed methodology is being validated against various state of the art key frame extraction algorithm which proves this methodology as reliable and faster complexion process for object re-identification.
Knowledge-Based Architecture for Recognising Activities of Older People

The world is facing an ageing population phenomenon, coupled with health and social problems, which affect older people's ability to live independently. This situation challenges the viability of health and social services. Smart home technology can play a significant role in easing the pressure on caregivers, as well as reduce the financial costs of health and social services. Activity of Daily Living (ADL) recognition is an essential step to translate sensor data into activities at high semantic levels. Supervised Machine Learning (ML) algorithms are the most commonly used techniques for this application. However, a common problem is a lack of availability of enough annotated data to train these algorithms. Collecting annotated data is expensive, time consuming, and may violate people's privacy. Intra- and inter-personal variation in performing complex activities is another challenge for an ML-based activity recognition approach. In this paper, a multi-layered knowledge-based architecture for recognising ADL in real-time is proposed. At the first stage, sensor data is pre-processed; events that describe changes in the environment are detected at the second stage, in which the sequence of events is used to recognise more semantically complex activities at the third stage. A new ADL ontology is proposed to model the knowledge related to the sensor platform and the targeted activities as the previously proposed ontologies were either designed to deal with specific sensor data, or they ignored the context environment information which is important in recognising complex activities.
A feasible trajectory planning algorithm for a network controlled robot subject to skid and slip phenomena

This paper aims to propose a feasible solution to the trajectory planning problem for a constrained skid-steering mobile robot whose control system, sensors and actuators are connected through a communication network. Operating environment is firstly discretized by a finite dimensional grid. Then a weighted graph, whose nodes are the above mentioned points and whose arcs denote the trajectory segments connecting points is defined. Finally an algorithm to obtain the shortest feasible succession of segments connecting given starting and ending points is proposed. Trajectory feasibility is guaranteed in terms of sufficient conditions involving the solution of semi-definite programming (SDP) problems. In order to show the effectiveness of the proposed approach, some numerical simulations are proposed.
A New Image-Based Visual Servo Control Algorithm for Target Tracking Problem of Fixed-Wing Unmanned Aerial Vehicle

This paper presents a new image-based visual servo (IBVS) control algorithm for the tracking of a static target by a fixed-wing unmanned aerial vehicle (UAV). The sensor used is a monocular camera fixed on the drone. The centroid coordinates of the target are extracted to be the inputs of the algorithm, while the control output is obtained to adjust the heading direction of the drone by the image Jacobian model. Besides, the problem that the shifts of the centroid caused by the changes of the UAV's attitude during tracking can be solved by a compensation mechanism. Through experiments by the hardware-in-the-loop (HIL) simulation, we evaluate the proposed algorithm. The results show that the proposed algorithm can effectively track the static target by a fixed-wing UAV.
Trust Factors and Insider Threats in Permissioned Distributed Ledgers

Permissioned distributed ledgers have recently captured the attention of organizations looking to improve efficiency, transparency and auditability in value network operations. Often the technology is regarded as trustless or trust-free, resulting in a false sense of security. In this work, we review the various trust factors present in distributed ledger systems. We analyze the different groups of trust actors and their trust relationships to the software layers and inherent components of distributed ledgers. Based on these analyses, we investigate how insiders may conduct attacks based on trust in distributed ledger components. To verify practical feasiblity of these attack vectors, we conduct a technical study with four popular permissioned distributed ledger frameworks: Hyperledger Fabric, Hyperledger Sawtooth, Ethereum and R3 Corda. Finally, we highlight options for mitigation of these threats.
Web Usability and Eyetracking

Eyetracking is one of several methodological tools researchers can employ when they need to evaluate in how far an interface or environment is amenable to a users’ tasks or action goals. This chapter provides an introduction to the field of web usability, what user research looks like, and how it is embedded in an applied research and design setting. We will then describe and discuss when and how eyetracking is useful as a methodology for measuring aspects of web usability, and what it can tell us about the interplay between users and interface. Different eyetracking measures are explained in the context of how they can help the research team to make inferences about users’ mental states and experience, and how these may be used to improve the interface design. This includes a discussion of some “myths” or common misconceptions about eyetracking in usability studies. A few brief case studies are given to illustrate eyetracking in applied web usability research.
Small Object Detection in Highly Variable Backgrounds

The analysis of imagery from outdoor remote sensing is a technique widely used for surveying and data gathering. This paper studies techniques to be deployed in small object localisation using Convolutional Neural Networks (CNN), with the aim to detect litter in outdoor non-urban imagery. The detection of small objects requires distinguishing features between foreground and background. A litter detection application has to counter high variability in the foreground, as litter is defined as a super-class of common objects, and the high variability found in a rural or coastal backgrounds. Remote sensing imagery of non-urban scenery does not offer high contrasting features, reducing the effect of normal object localisation techniques.
Visualization of Programming Skill Structure by Log-Data Analysis with Decision Tree

To evaluate the ability of a programmer in universities or staff agencies, evaluators conduct examination in which applicants solve problems, or they refer to the applicants’ GitHub and review their code. However, the collected source code is evaluated by a human under present conditions. This leads to two problems: many source codes cannot be evaluated simultaneously, and it is difficult to maintain consistency in the evaluation criteria among evaluators. We propose methods to estimate the current skill of the programmer, which can be used by the evaluators to understand an applicant’s skill by analyzing the collected source code automatically. In particular, this study visualizes the feature of the students’ source code in term of quarters using a decision tree to estimate the programming skill.
3D Multi-UAV Collaboration Based on the Hybrid Algorithm of Artificial Bee Colony and A*

Multi-UAV cooperative operation has more advantages in penetrating enemy defense systems, detecting targets and attacking missions. Firstly, a 3D multi-UAV track planning method based on the hybrid algorithm of artificial bee colony and A* is presented. Theoretical and simulation researches have shown that the hybrid algorithm has better performance in avoiding threats and smaller track cost compared to A* algorithm. Secondly, the adaptive time collaboration under the 3D multi-UAV collaborative track planning based on the hybrid algorithm is studied. According to the time arrange, the adaptive time coordination state is divided into simultaneous arrival, sequential arrival, and uncoordinated arrival. In the case of sequential arrival, we focus on the iterative selection of the arrival time difference instead of specified time intervals, which can maximize the possibility of the multi-UAV sequential arrival and enhance the ability of coordinated attack.
“Computer what's your favourite colour?” children's information‐seeking strategies in the classroom

ABSTRACT This research investigates children's information seeking in primary and intermediate school classrooms as seen by their teachers. We report the results of a series of semi‐structured interviews with teachers of pre‐teen children. The teachers discussed specific issues that children encounter during information search, including query construction and information triage difficulties. Further issues were identified during the interviews regarding motivation, frustration and poor strategies for overcoming barriers. We compare the results of our teacher interviews reported here with the results of an interview study with children. This paper concludes that even with significant advancements in technology, teachers and children still require further assistance in order to succeed in their inquiry practices.
THOTH: Neural Translation and Enrichment of Knowledge Graphs

Knowledge Graphs are used in an increasing number of applications. Although considerable human effort has been invested into making knowledge graphs available in multiple languages, most knowledge graphs are in English. Additionally, regional facts are often only available in the language of the corresponding region. This lack of multilingual knowledge availability clearly limits the porting of machine learning models to different languages. In this paper, we aim to alleviate this drawback by proposing THOTH, an approach for translating and enriching knowledge graphs. THOTH extracts bilingual alignments between a source and target knowledge graph and learns how to translate from one to the other by relying on two different recurrent neural network models along with knowledge graph embeddings. We evaluated THOTH extrinsically by comparing the German DBpedia with the German translation of the English DBpedia on two tasks: fact checking and entity linking. In addition, we ran a manual intrinsic evaluation of the translation. Our results show that THOTH is a promising approach which achieves a translation accuracy of 88.56%. Moreover, its enrichment improves the quality of the German DBpedia significantly, as we report +18.4% accuracy for fact validation and +19% F $$_1$$ for entity linking.
Evaluation of Ultrasound Haptics as a Supplementary Feedback Cue for Grasping in Virtual Environments

This paper presents an evaluation of ultrasound mid-air haptics as a supplementary feedback cue for grasping and lifting virtual objects in Virtual Reality (VR). We present a user study with 27 participants and evaluate 6 different object sizes ranging from 40 mm to 100 mm. We compare three supplementary feedback cues in VR; mid-air haptics, visual feedback (glow effect) and no supplementary feedback. We report on precision metrics (time to completion, grasp aperture and grasp accuracy) and interaction metrics (post-test questionnaire, observations and feedback) to understand general trends and preferences. The results showed an overall preference for visual cues for bigger objects () while ultrasound mid-air haptics were preferred for small virtual targets ().
SmartTab: A Design &amp; Implementation of Tablet for Learning Purposes based on PyQT framework

Handheld devices and smartphone at present are being used as a high potential learning devices and due to which the enhancement of academic curriculum based on smart devices have major impact on quality education. The increased popularity of tablets made its usage in education field also. This paper focuses on the design and development of a SmartTab, which is a prototype of a Smart Tablet having characteristics of both a tablet and a laptop. It is one of the tablet working with a desktop OS (Raspbian), which makes it different from other tablets that are designed with mobile OS. It is designed with some different aspects such as providing ports for input/output, connections for any display device such as LCD, etc. Thus it is different from traditional tablets, but includes features of them as well along with some new features. Traditional tablets mainly serves as an entertainment medium, while SmartTab can be useful for both entertainment as well as learning purposes. This paper presents PyQT framework as an effective tool which provides the cross-platform design & development of Smart Learning System.
Speech spoofing countermeasures based on source voice analysis and machine learning techniques

Automatic speaker verification (ASV) [7] systems are susceptible to malicious attacks. It discredit the performance of a standard ASV system by increasing its false acceptance rates. This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed signals. The new countermeasure is based on the analysis of a sequence of acoustic feature vectors using the glottal inverse filtering. In the proposed method, speech is decomposed into a glottal source signal and model the vocal tract filter through glottal inverse filtering. The IAIF desriptors are constructed and are used as features. Support Vector Machines (SVM) classifier and Extreme learning machine (ELM) are used to classify the obtained features as genuine or spoofed. It is hoped that the proposed method can help to detect the genuine speech from the spoofed one.