Reviewerly/exHarmony huggingface dataset:
Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A

Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in [email protected]%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of [email protected]%FAR.
Navigation of Three Cooperative Object-Transportation Robots Using a Multistage Evolutionary Fuzzy Control Approach

This article proposes a new multistage evolutionary fuzzy control configuration and navigation of three-wheeled robots cooperatively carrying an overhead object in unknown environments. Based on the divide-and-conquer technique, this article proposes a stage-by-stage evolutionary obstacle boundary following (OBF) fuzzy control of each of the three robots through multiobjective continuous ant colony optimization. In the first stage, a set of evolutionary nondominated fuzzy controllers (FCs) for a single robot (a leader robot) in the execution of the OBF behavior is learned. In the second stage, a follower robot is controlled by two evolutionary FCs in combination with a switched compensation FC so that the leader and follower robots can cooperatively transport an object while executing the OBF behavior along obstacles containing corners with right angles. In the third stage, the third robot functions as an accompanying robot and is learned to enter into a predicted triangular formation with the leader-follower robots to transport a larger object while executing the OBF behavior. In the navigation of the three object-transportation robots, a new cooperative behavior supervisor is proposed to coordinate the learned OBF behavior and a target seeking behavior. Successful navigations in simulations and experiments verify the effectiveness of the multistage evolutionary fuzzy control approach and navigation scheme.
Autonomous Decision-Making Generation of UAV based on Soft Actor-Critic Algorithm

In this paper, we utilize deep reinforcement learning algorithm Soft Actor-Critic (SAC) to solve the autonomous decision-making problem of Unmanned Aerial Vehicle (UAV). Firstly, the decision-making problem of UAV is abstracted into a game scenario, and the simulation environment is built based on tensorflow, pygame, etc. Secondly, the decision-making problem of UAV is modeled as Markov Decision Process (MDP), the reinforcement learning framework is constructed, and the SAC algorithm is connected with the simulation environment. Finally, the model begins to be trained, so that the UAV learns the actions of the task, finds the optimal strategy, and generates autonomous decision-making capabilities based on SAC algorithm. It can take full advantage of reinforcement learning (RL) in sample-free learning. Facing the complex and unknown decision-making environment, the SAC algorithm can make the UAV continuously summarize experience in the process of interacting with the environment and make the best strategic choice. The effectiveness of autonomous decision-making based on RL is verified through simulation experiments.
Improving fine-tuned question answering models for electronic health records

The prevalence of voice assistants has strengthened the interest in a question answering for the medical domain, allowing both patients and healthcare providers to enter a question naturally and pinpoint useful information quickly. However, a large number of medical terms make the creation of such a system a demanding task. To address this challenge, we explore transfer learning techniques for constructing a personalized EHR-QA system. The goal is to answer questions regarding a discharge summary in an electronic health record (EHR). We present the experiments with a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model fine-tuned on different tasks and show the results obtained to provide insights into learning effects and training effectiveness.
Deep Symbolic Learning and Semantics for an Explainable and Ethical Artificial Intelligence

The main objective of this research is to investigate new hybrid neuro-symbolic algorithms for the construction of an open-source Deep Symbolic Learning framework that allows the training and application of explainable and ethical Deep Learning models. This framework will be supported by an ontology and a layer model in which it is taken into account which user is responsible for interpreting each of the output results according to his or her role, considering, also, the ethical implications of those results.
Integrated Expected Likelihood Particle Filters

In this paper, we discuss the derivations, implementations and performance of target tracking algorithms for a single-target single-sensor system estimating the state, covariance and existence probability of a target. Given the target exists, we simulate measurements of the target with a given probability of detection, along with false measurements (clutter) parametrised by a clutter density. We evaluate the performance of the algorithms by computing the area under Receiver Operating Characteristic (ROC) curves against a range of clutter density values. We give particular attention to the effectiveness of correctly inferring the presence or absence of the target. We select the Integrated Probabilistic Data Association Filter (IPDAF) and the Integrated Expected Likelihood Particle Filter (IELPF) algorithms, with the IELPF implementing a near-optimal proposal which uses the current scan of measurements as well as a prior proposal for comparison. Simulation results indicate the performance of the IPDAF exceeds that of the preexisting particle filter implementing a prior proposal, but a novel particle filter using a near-optimal proposal and a modest number of particles outperforms the IPDAF.
BaKGraSTeC: A Background Knowledge Graph Based Method for Short Text Classification

Short text classification is an important task in the area of natural language processing. Recent studies attempt to employ external knowledge to improve classification performance, but they ignore the correlation between external knowledge and have poor interpretability. This paper proposes a novel Background Knowledge Graph based method for Short Text Classification called BaKGraSTeC for short, which can not only employ external knowledge from a knowledge graph to enrich text information, but also utilize its structural information through a graph neural network to promote the understanding of texts. Specifically, we construct a background knowledge graph based on training data, then we propose a novel architecture that integrates background knowledge graph into a graph neural network to model and capture implicit interactions between its concepts and classes. Besides, we propose an attention mechanism considering both similarity and co-occurrence between concepts and classes to identify the informative concepts in texts. Our experimental results demonstrate the effectiveness with good interpretability of BaKGraSTeC through using external knowledge and their structural information for short text classification.
Providing architectural support for building privacy-sensitive smart home applications

In this thesis, we plan to introduce a new IoT app development framework named Peekaboo, which aims to make it much easier for developers to get the granularity of data they actually need rather than always requesting raw data, while also offering architecture support for building privacy features across all the apps. Peekaboo's architectural design philosophy is to factor out repetitive data pre-processing tasks (e.g., face detection, frequency spectrum extraction) from the cloud side onto a user-controlled hub, and support them as a fixed set of open source, reusable, and chainable operators. These operators pre-process raw data to remove unneeded sensitive user information before the data flow to the cloud (and out of the users' control), thus reducing data egress and many potential privacy risks for users. Further, all the IoT apps built with Peekaboo share a common structure of the chainable operators, making it possible to build consistent privacy features beyond individual apps.
Wrapping a NoSQL Datastore for Stream Analytics

With the advent of the Industrial Internet of Things (IIoT) and Industrial Analytics, numerous application scenarios emerge, where business and mission-critical decisions depend upon large scale analytics of sensor streams. However, very large volumes of data from data streams generated at a high rate pose substantial challenges in providing scalable analytics from existing Database Management Systems (DBMS). While scalability can be provided by high-performance distributed datastores, due to the simple query operations, access to high-level query-based data analytics is usually limited. This work combines high-level query-based data analytics capabilities with high-performance distributed scalability by applying a wrapper-mediator approach. The Amos II extensible main-memory DBMS provides online query processing data analytics engine in front of the MongoDB distributed NoSQL datastore to support large-scale distributed data analytics over persisted data streams. Thus, the implemented system enables query-based online data stream analytics over persisted data streams stored/logged in distributed NoSQL datastores.
UI Screens Identification and Extraction from Mobile Programming Screencasts

Mobile applications demand is on the rise, leading to more programmers learning to develop or having to maintain this kind of programs. Developers often refer to online resources to find inspiration or answers to questions they have about mobile programming topics and screencasts are a popular resource. However, given the multitude of screencasts available, it can be difficult to quickly comprehend which of the many videos is relevant to one's needs.
Multimodal Fusion with Co-attention Mechanism

Because the information from different modalities will complement each other when describing the same contents, multimodal information can be used to obtain better feature representations. Thus, how to represent and fuse the relevant information has become a current research topic. At present, most of the existing feature fusion methods consider the different levels of features representations, but they ignore the significant relevance between the local regions, especially in the high-level semantic representation. In this paper, a general multimodal fusion method based on the co-attention mechanism is proposed, which is similar to the transformer structure. We discuss two main issues: (1) Improving the applicability and generality of the transformer to different modal data; (2) By capturing and transmitting the relevant information between local features before fusion, the proposed method can allow for more robustness. We evaluate our model on the multimodal classification task, and the experiments demonstrate that our model can learn fused featnre representation effectively.
An Assessment of Data Location Vulnerability for Human Factors Using Linear Regression and Collaborative Filtering

End-user devices and applications (data locations) are becoming more capable and user friendly and are used in various Health Information Systems (HIS) by employees of many health organizations to perform their day to day tasks. Data locations are connected via the internet. The locations have relatively good information security mechanisms to minimize attacks on and through them in terms of technology. However, human factors are often ignored in their security echo system. In this paper, we propose a human factor framework merged with an existing technological framework. We also explore how human factors affect data locations via linear regression computations and rank data location vulnerability using collaborative filtering. Our results show that human factors play a major role in data location breaches. Laptops are ranked as the most susceptible location and electronic medical records as the least. We validate the ranking by root mean square error.
On the Impact of Different Kernels and Training Data on a Gaussian Process Approach for Target Tracking

The application of multiple target tracking algorithms has exponentially increased during the last two decades. Recently, model-free approaches, such as Gaussian process regression and convolutional neural networks, have been developed for target tracking. This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian process motion tracker [1]. The paper also provides design guidelines on the various aspects of the above-mentioned tracking method.
Joint Human Pose Estimation and Stereo 3D Localization

We present an end-to-end trainable Neural Network architecture for stereo imaging that jointly locates and estimates human body poses in 3D. Our method defines a 2D pose for each human in a stereo pair of images and uses a correlation layer with a composite field to associate each left-right pair of joints. In absence of a stereo pose dataset, we show that we can train our method with synthetic data only and test it on real-world images (i.e., our training stage is domain invariant). Our method is particularly suitable for autonomous vehicles. We achieve state-of-the-art results for the 3D localization task on the challenging real-world KITTI dataset while running four times faster.
An improved heuristic technique for nearest neighbor realization of quantum circuits in 2D architecture

Last couple of years has witnessed tremendous advancements in the field of quantum computing and even it has started providing technological footprints in the design industry. Though advancements in the physical implementation of quantum circuits has taken a giant leap but it has faced with several design challenges and one such design constraint is Nearest Neighbor (NN) criteria which demands the operating qubits of the quantum gates to be adjacent. Focusing on the design issue, here, we show a heuristic design technique for efficient transformation of quantum circuits to NN based designs in 2D configuration. Our entire strategy is based on initial qubit mapping policy, where we have introduced three different mapping techniques 1) Influence Index, 2) Adjacency Matrix and 3) Distance Ratio based strategy. After placing the qubits in appropriate positions, we also undertake a dynamic windowing based local reordering scheme to further reduce the SWAP requirement in the designs. At the end of the work, to check the effectiveness of our transformation algorithms, we have tested a wide range of benchmarks over our algorithms and a comparative study over state-of-the-art design techniques also has been undertaken.
Introducing Streaming into Linear Algebra-based Sparse Graph Algorithms

GraphBLAS is a new package designed to provide a standard set of building blocks for graph algorithms based formally in the language of linear algebra. This paper suggests some extensions of the underlying math that would enhance GraphBLAS' ability to stream updates into a computation without a bulk recomputation, and at greatly reduced computational complexity. The process is applied to several examples.
Dial/Hybrid Cascade 3DResUNet for Liver and Tumor Segmentation

Liver tumor segmentation is an important step in diagnosis of liver cancer. In this work, a cascaded fully convolutional network (FCN) is proposed based on improved 3DResUNet for automatic segmentation of liver and liver tumors. The first FCN is trained to segment the liver as region of interest (ROI) input for the second FCN, which solely segment the tumor from the predicted liver region. Based on 3DResUNet, a Dial-3DResUNet is designed for liver segmentation which combines hybrid dilated convolution to capture global features, a Hybrid-3DResUNet is developed for tumor segmentation, the model consists of Hybrid-3D convolution to effectively extract 3D features while greatly reducing the amount of parameters and decreasing the difficulty of model optimization and the risk of overfitting. Ablation study is conducted on magnetic resonance (MR) data provided by the Chinese PLA General Hospital to demonstrate the effectiveness of the Dial/Hybrid-3DResUNet. In addition, we evaluate our model on 3DIRCADb dataset and achieved a dice global score of 0.958 and 0.742 on liver and liver tumor, respectively.
Multi-label Learning with a Cone-Based Geometric Model

Recent approaches for knowledge-graph embeddings aim at connecting quantitative data structures used in machine learning to the qualitative structures of logics. Such embeddings are of a hybrid nature, they are data models that also exhibit conceptual structures inherent to logics. One motivation to investigate embeddings is to design conceptually adequate machine learning (ML) algorithms. This paper investigates a new approach to embedding ontologies into geometric models that interpret concepts by closed convex cones. As a proof of concept this cone-based embedding was implemented in a ML algorithm for weak supervised multi-label learning. The system was tested with the gene ontology and showed a performance similar to comparable approaches, but with the advantage of exhibiting the conceptual structure underlying the data.
Modified listening group method as a knowledge-sharing and learning mechanism in agricultural communities in the Philippines

Purpose: The aim of this study is to explore if the modified listening group method that originated in Canada can enhance learning and sharing amongst farmers in a group learning setup.Design/Methodology/Approach: The study, participated in by 111 rice farmers, was conducted in Farmer Field School sites of PhilRice in the provinces of Agusan Del Sur, Bohol, Ilocos Norte, Negros Occidental, and Tarlac. It employed a quasi-experimental research design.Findings: Overall, we found that the modified listening group method enables learning of technical concepts and provides avenues for farmers to collectively forward their agenda to the government. Taking on a more critical approach, however, and moving this research forward, we argue that there is a need to scrutinise the types of knowledge shared and muted during the course of exchanges amongst farmers. Several questions relating to power relations in knowledge-sharing are advanced in this research.Theoretical Implications: This paper contributes to addressing the dearth of studies from developing countries on the use of listening groups in agricultural extension.Practical Implications: The study explores and offers a cost-effective strategy to enhance learning and sharing in a group learning setup amongst farmers.Originality/Value: The study explored how an old advisory method like the listening groups may be revived and modified to facilitate learning in a group learning setup.
Online Trajectory Optimization for UAV in Uncertain Environment

The stability of the flight attitude and the rationality of the path are two critical indicators to measure the application of the UAV. Especially for the Unmanned Aerial Vehicle (UAV), the stability of attitude, and the yaw angle without external interference play a vital role in the whole UAV system. Some reasons can have an influence on the performance of the UAV; one is the problem of trajectory tracking; another is uncertain in the environment, the UAV model will be vulnerable to external disturbances; therefore, a new nonlinear back-step slide mode controller combined with the extended Kalman filter-simultaneous localization and mapping (EKF-SLAM) algorithm and hp adaptive pseudo-spectral algorithm is proposed to estimate the uncertain disturbances and to realize the online optimization in the actual trajectory of the UAV. Computer simulations are presented to show how the proposed method works. The UAV principles are determined, and the nonlinear motion mathematical model of the UAV is established. The experimental data are analyzed to prove the correctness of the theoretical analysis.
CSH: A Post-quantum Secret Handshake Scheme from Coding Theory

In secret handshake schemes, the members in the same organization can anonymously authenticate each other and commonly negotiate a secret key for communication. Since its proposing in 2003, secret handshake schemes become an important privacy protection cryptographic technique on internet applications. In this paper, a secret handshake scheme based on coding theory (we call $$\mathsf {CSH}$$ ) is presented. This is the first code-based secret handshake scheme. $$\mathsf {CSH}$$ is constructed by combining the CFS signature system and Stern’s identification system, thus the security of $$\mathsf {CSH}$$ relies on the syndrome decoding problem just like the two above systems. Moreover, as far as we know, $$\mathsf {CSH}$$ is the first scheme to use a generic construction of Fiat-Shamir paradigm in secret handshake schemes. This may lead to a more generic framework construction.
An exploratory examination of organizational insiders’ descriptive and normative perceptions of cyber-relevant rights and responsibilities

Within the field of organizational cybersecurity, much attention has been given to insider compliance and non-compliance with the information security policies (ISPs) set forth by their organizations. Most of these efforts apply theoretical foundations based on self-interest, personal incentive, and cost-benefit calculations to explain compliance and non-compliance motives. We take a different approach to understand insiders’ ISP compliance by exploring how insiders view their rights and responsibilities related to security-relevant behaviors. Relying on Deonance Theory, we assess the extent to which insiders categorize a wide variety of behaviors that are or can be implemented in corporate ISPs according to several deontic conditional operators (e.g., nature of perceived requiredness). These operators form the basis for a rights and responsibility framework. We find that out of 38 unique security-relevant behaviors, 22 exhibit one or more forms of potential moral “gray area” patterns. Among these patterns, significant differences between insiders’ descriptive (i.e., “is”) and normative (i.e., “should be”) assessments of rights and responsibilities perceptions are particularly interesting. Our findings shed additional light on insiders’ compliance with organizational ISPs when those ISPs place increased restrictions on what the insider must or must not do.