Reviewerly/exHarmony huggingface dataset:
Multi-Stream Siamese and Faster Region-Based Neural Network for Real-Time Object Tracking

Object tracking is a challenging task in computer vision based intelligent transportation systems. Recently, Siamese based object tracking methods have attracted significant attention due to their highly efficient performance. These tracking methods usually train a Siamese network to match the initial target patch of the first frame with candidates in a new frame. In these methods, the offline training of the deep neural network and the online instance searching are effectively combined. However, these methods usually do not include template update or object re-identification, which easily results in the drift problem. In this paper, we propose a novel real-time object tracking method to overcome the above problems by effectively combining a multi-stream Siamese network and a region-based convolutional neural network. Specifically, a novel multi-stream Siamese network is proposed to search the target and update the instance template in a new frame. In addition, a faster region-based convolutional neural network detector is used to perform object re-identification in order to improve the tracking performance by making full use of the object category information. These two networks are tightly coupled to ensure that the proposed tracking method has high efficiency and strong discriminative capability. Experimental results on several object tracking benchmarks show that our tracking method can effectively track vehicles and pedestrians in video sequences by exploiting the object category information. The proposed tracking method achieves real-time operations and outperforms several other state-of-the-art methods.
A Two-Phase Dynamic Throughput Optimization Model for Big Data Transfers

The amount of data transferred over dedicated and non-dedicated network links has been increasing much faster than the increase in the network capacity. On the other hand, the current data transfer solutions fail to guarantee even the promised achievable transfer throughput. In this article, we propose a novel two-phase dynamic throughput optimization model based on mathematical modeling with offline knowledge discovery/analysis and adaptive online decision making. In the offline analysis, we mine historical transfer logs to perform knowledge discovery about the transfer characteristics. The online phase uses the discovered knowledge from the offline analysis along with the real-time investigation of the network condition to optimize the protocol parameters. As the real-time investigation is expensive and provides partial knowledge about the current network status, our model uses historical knowledge about the network and data characteristics to reduce the real-time investigation overhead while ensuring near-optimal throughput for each transfer. Our novel approach is tested over different networks with different datasets, and it has outperformed its closest competitor by 1.7x and the default case by 5x. It also achieved up to 93 percent accuracy compared to the optimal achievable throughput possible on those networks.
A view of estimation of distribution algorithms through the lens of expectation-maximization

We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples. Because EM sits on a rigorous statistical foundation and has been thoroughly analyzed, this connection provides a new coherent framework with which to reason about EDAs.
A privacy-aware digital forensics investigation in enterprises

Stricter policies, laws and regulations for companies on the handling of private information arise challenges in the handling of data for Digital Forensics investigations. This paper describes an approach that can meet necessary requirements to conduct a privacy-aware Digital Forensics investigation in an enterprise. The core of our approach is an entropy-based identification algorithm to detect specific patterns within files that can indicate non-private information. Files containing sensitive information are excluded systematically. This privacy preserving method can be integrated into a Digital Forensics examination process to prepare an image which is free from private as well as critical information for the investigation. The approach demonstrates that investigations in enterprises can be supported and improved by adapting existing algorithms and processes from related subject areas to implement privacy preserving measures into an investigation process.
Nym Credentials: Privacy-Preserving Decentralized Identity with Blockchains

Nym credentials solve the problem of privacy-enhanced authentication amongst a decentralized open-ended ecosystem of services. Many blockchain systems offer decentralized services, but at the cost of revealing all transactions in a public ledger, which is clearly not suitable for high-value transactions involving personal data. Nym credentials deploy anonymous authentication credentials as an identity system that maintains user privacy. Due to the validators being decentralized, the system is compatible with blockchain-based systems. Tokens can be used to show the "right to access" privacy-enhanced services. Nym validators transform these tokens into Nym credentials that validated publicly in a decentralized manner via a modified Coconut signature scheme capable of the open-ended embedding arbitrary attributes.
Improved course recommendation algorithm based on collaborative filtering

As multidisciplinary educational interest increases, it is more and more important to support students course decision. This paper proposes a new novel recommended algorithm based on collaborative filtering for the course recommender to help student's decision. In this algorithm, the improved cosine similarity is used, according to the history of students' course selection records, and the better accuracy is obtained in the recommendation task, which meets the needs of users. In addition, both text vector and user behavior record are used to improve the calculation of course similarity. This paper evaluates 2022 students' 18457 records and 309 courses' real data. The experimental results show that the algorithm has a good effect on accuracy, recall rate and F1-score index.
Braille Block Recognition Using Convolutional Neural Network and Guide for Visually Impaired People

Braille blocks are the main tools to assist visually impaired people. However, it is difficult for visually impaired people to find braille blocks. As a matter of fact, there are many accidents such that visually impaired people fall from a platform. Therefore, it is important to detect braille blocks and inform them of where braille blocks are. This research aims to guide visually impaired people to braille blocks. In order to detect braille blocks, a camera is used and Convolutional Neural Network (CNN) is applied as the method of image recognition. In this paper, the theory of CNN and guide method are explained and the structure of CNN used in the experiment is shown. In the experiment, CNN is established learnt from training data and the recognition accuracy of braille blocks is confirmed with test data. The results of the experiments show that the test accuracy is approximately 94%.
Noise-based Selection of Robust Inherited Model for Accurate Continual Learning

There is a growing demand for an intelligent system to continually learn knowledge from a data stream. Continual learning requires both the preservation of previous knowledge (i.e., avoiding catastrophic forgetting) and the acquisition of new knowledge. Different from previous works that focus only on model adaptation (e.g., regularization, network expansion, memory rehearsal, etc.), we propose a novel training scheme named acquisitive learning (AL), which emphasizes both the knowledge inheritance and knowledge acquisition. AL starts from an elaborately selected model with pre-trained knowledge (the inherited model) and then adapts it to new data using segmented training. The selection is achieved by injecting random noise to various inherited models for better model robustness, which promises higher accuracy in further knowledge acquisition. The approach is validated by the visualization of the loss landscape and quantitative roughness measurement. The combination of the selective inherited model and knowledge acquisition reduces catastrophic forgetting by 10X on the CIFAR-100 dataset.
Possibilistic admissible region using outer probability measure theory

The admissible region method provides a reliable orbit initialization framework for space objects with extremely limited observations. In general, the uncertainty of the admissible region is either ignored or treated as a probability distribution. However, the probabilistic characterization requires that one possess exact knowledge of the distribution of all random errors, e.g., the observation process, the constraint parameters, and the observer related information, which may not be available in realistic scenarios. In this paper, we explore the use of Outer Probability Measure (OPM) theory to achieve a more truthful characterization of the uncertainty of the admissible region, dubbed the Possibilistic Admissible Region (PAR+). Considering that the uncertainty of the constraints is epistemic and modeled by possibility functions, a rigorous possibilistic uncertainty representation is achieved without the need of known random statistics of the system. To wit, the method presented herein can be appropriately interpreted as initializing our inference of a dynamical system with a so-called uninformative prior. The developed PAR+ models the uncertainty of the admissible region as an OPM represented by a collection of particles. It provides a method for space object tracking in the presence of assumed ignorance and unknown randomness (i.e. uninformative prior). The PAR+ for the commonly used angles-only and radar measurements are developed and illustrated using simulated scenarios.
The Mystery of the Failing Jobs: Insights from Operational Data from Two University-Wide Computing Systems

Node downtime and failed jobs in a computing cluster translate into wasted resources and user dissatisfaction. Therefore understanding why nodes and jobs fail in HPC clusters is essential. This paper provides analyses of node and job failures in two university-wide computing clusters at two Tier I US research universities. We analyzed approximately 3.0M job execution data of System A and 2.2M of System B with data sources coming from accounting logs, resource usage for all primary local and remote resources (memory, IO, network), and node failure data. We observe different kinds of correlations of failures with resource usages and propose a job failure prediction model to trigger event-driven checkpointing and avoid wasted work. Additionally, we present user history based resource usage and runtime prediction models. These models have the potential to avoid system related issues such as contention, and improve quality of service such as lower mean queue time, if their predictions are used to make a more informed scheduling decision. As a proof of concept, we simulate an easy backfill scheduler to use predictions of one of these models, i.e., runtime and show the improvements in terms of lower mean queue time. Arising out of these observations, we provide generalizable insights for cluster management to improve reliability, such as, for some execution environments local contention dominates, while for others system-wide contention dominates.
On Incentive Compatible Role-Based Reward Distribution in Algorand

Algorand is a recent, open-source public or permissionless blockchain system that employs a novel proof-of-stake Byzantine consensus protocol to efficiently scale the distributed transaction agreement problem to billions of users. Despite its promise, one relatively understudied aspect of this protocol has been the incentive compatibility of its reward sharing approach, without which cooperation among rational network users cannot be guaranteed, resulting in protocol failure. This paper is the first attempt to address this problem. By carefully modeling the participation costs and rewards received within a strategic interaction scenario in Algorand, we first show that even a small number of non-participating users (due to insufficiency of the expected incentives) can result in the network failing to append new transaction blocks. We further show that this effect, which was observed in simulations, can be formalized by means of a game-theoretic model that realistically captures the strategic interactions between users in Algorand. Specifically, we formally prove that mutual cooperation under the currently proposed reward sharing approach in Algorand is not a Nash equilibrium. To remedy this, we propose a novel reward sharing approach for Algorand and formally show that it is incentive-compatible, i.e., it can guarantee cooperation within a group of selfish users. Extensive numerical and Algorand simulation results further confirm our analytical findings. Moreover, these results show that for a given distribution of stakes in the network, our reward sharing approach can guarantee cooperation with a significantly smaller reward per round.
Mirco-earthquake source depth detection using machine learning techniques

Discrimination of mirco-earthquake on source depth plays an important role in the field of micro-seismic monitoring. Conventional machine learning methods for data classification rely on carefully hand-engineered features that are vulnerable to low signal-to-noise ratio. Convolutional neural networks (CNNs) demonstrate some merits in dealing with structured data modelling where a set of meaningful features can be automatically extracted from sample learning. This paper explores the use of machine learning techniques for discrimination between deep and shallow mirco-seismic events. A benchmarked dataset including 444 micro-earthquakes from an underground cavern collapse in South Louisiana is employed for performance evaluation in this study, where several feature-based classifiers are compared against the CNN classifier. Empirical results show that the deep learning method outperforms the conventional classification techniques in discriminating the source depth.
Software Abstractions and Human-Cyber-Physical Systems Architecture Modelling

It is over fifty years since the subject discipline of software engineering and more than forty years from when the area of formal methods have been established. During this period, the academic community has accomplished extensive research in foundations and methods of software engineering, as well as developing and teaching a large body of software engineering knowledge and techniques. At the same time, the IT industry has produced larger, more complex, and better (in many aspects) software systems. Yet, these large projects are largely developed using a trial and error approach, without systematic use of the developed software engineering methods and tools. The cost of these projects is high, the percentage of project delay and cancellation is significant, and the dependability of the systems is low in many requirements. The most serious problem of this ad hoc development approach is that the development process is not repeatable and the systems developed are not well evolvable. This problem is particularly crucial for the design and implementation of modern networked distributed software systems, known as Human-Cyber-Physical Systems (HCPS). In this tutorial paper, we reflect the development of software engineering through software abstractions and show that these abstractions are integral in the notion of software system architectures. We discuss the importance of architecture modelling and argue for a seamless combination of informal and formal activities in the modelling and design of the architecture. A point that we make is that it is important to engineer systems using formal methods in relation to the definition and management of development processes, and how a model of the software architecture, with rich semantics and refinement relations, plays an important role in this process. We consider development of two typical types of software components and use examples to discuss the traditional processes for their domain modelling and software requirements modelling. We then propose to combine these modelling approaches and this naturally leads to a unified modelling process for HCPS architecture modelling, design, and evolution. Based on the unified processes, we outline a framework in engineering formal methods for HCPS modelling, including the mapping of the system architecture to the technology architecture and organization of the development team with the expertise required, and decide the appropriate formal methods and tools to be used.
Recursive Hybrid Fusion Pyramid Network for Real-Time Small Object Detection on Embedded Devices

This paper proposes a novel RHF-Net (Recursive Hybrid Fusion pyramid network) to solve the problem of small object detection on real-time embedded devices. Though the object detection accuracy rate is improved by a large margin with SoTA (State-of-The-Art) models, e.g., SSD, YOLO, RetinaNet, and RefineDet, they are still problematic for small object detection and inefficient on embedded systems. One novelty of the RHF-Net is a bidirectional fusion module) that allows to fuse feature maps with both the top-down and bottom-up directions to generate flexible FPs for small object detection. This module can be easily integrated to any feature pyramid based object detection model. Another novelty of this net is a recursive concatenation and reshaping module which can recursively concatenate not only high-level semantic features from deep layers but also reshape spatially richer features from shallower layers to prevent small objects from disappearing. RHF-Net net adopts computationally low-cost and feature preserving operations in the fusion, thus it is efficient and accurate even on embedded devices. The superiority of RHF-Net is investigated on the COCO benchmark and UAVDT dataset in terms of mAP and FPS.
SOMA-CL for competition on single objective bound constrained numerical optimization benchmark

This paper describes a competition entry for the CEC 2020 benchmark set on a single objective bound-constrained numerical optimization at The Genetic and Evolutionary Computation Conference (GECCO) 2020 by a novel algorithm titled Self-organizing Migrating Algorithm with CLustering-aided migration (SOMA-CL).
Velocity Obstacle Based Strategy for Multi-agent Collision Avoidance of Unmanned Aerial Vehicles

Unmanned Aerials Vehicles (UAVs), most known as drones, recently gained a lot of interest in several domains of civil applications. With the number of operating drones expected to quickly rise in the next few years comes the need to develop efficient collision avoidance solutions. To ensure the applicability of the collision avoidance solutions, given the limitations of UAVs, they must rely on a reliable source of information, and must be computationally efficient. This paper aims at providing an implementation of the Velocity Obstacle collision avoidance method, based on the information transmitted by the other UAVs. Multi-agent based simulations using the simulation software AirSim and the multi-agent platform SARL are proposed to assess the validity of the proposed solution.
Facial Expression Recognition based on The Fusion of CNN and SIFT Features

Facial expression recognition is an important part of computer vision. In order to improve the accuracy of expression recognition, and at the same time overcome the problem that a single feature cannot fully represent the details of facial expressions, this paper proposes CNN and SIFT feature fusion algorithms: 1) using a custom CNN network, combined with the idea of the Inception module, that is, adding 1×1 convolution, can more efficiently use computing resources, extract more global facial expression information under the same amount of calculation; 2) use cascade regression to calibrate the facial facial structure points, and then extract SIFT features, so that the key points are concentrated on expression contributions In a large area, the two features merge with each other and complement each other. Finally, the fused features are classified using Softmax to improve the accuracy of facial expression recognition. Tested on the CK+, JAFFE and FER2013 data sets, the experimental results show that this method is an efficient method of facial expression recognition.
Expressive ASL Recognition using Millimeter-wave Wireless Signals

Over half a million people in the United States use American Sign Language (ASL) as their primary mode of communication. Automatic ASL recognition would enable Deaf and Hard of Hearing (DHH) users to interact with others who are not familiar with ASL as well as voice-controlled digital assistants (e.g., Alexa, Siri, etc.). While ASL recognition has been extensively studied, there is a little attention given to recognition of ASL non-manual body markers. The non-manual markers are typically expressed through head, torso and shoulder movements, and add essential meaning and context to the signed sentences. In this work, we present ExASL, a sentence-level ASL recognition system using millimeter-wave radars. ExASL can recognize manual markers (hand gestures) and non-manual markers (head and torso movements). It utilizes multi-distance clustering to recognize body parts and cluster mmWave point clouds. We then present a multi-view deep learning algorithm that can learn from clustered body part representation for an expressive sentence-level recognition. Our evaluation shows that ExASL can recognize ASL sentences with a word error rate of 0.79%, sentence error rate of 1.25%, and non-manual markers with an accuracy of 83.5%.
Augmented Reality-based application to foster sustainable agriculture in the context of aquaponics

Population growth implies the need to produce more food. This increases pressure on the planet's resources, which drives climate change and challenges environmental sustainability. Hence, it is important to promote sustainable agriculture strategies that contribute to global food and nutrition security while protecting natural resources. These strategies should be guided from an educational perspective that motivates people to develop positive bonds with nature. In this regard, augmented reality has emerged as a technology with the potential to improve environmental education programs. This paper presents the development and evaluation of an augmented reality-based educational application, whose purpose is to foster sustainable agriculture in the context of aquaponics. The application was developed using Unity and Vuforia following the principles of instructional design. To evaluate the effectiveness of the application, we designed a pilot study with 10 volunteers. The results indicate that the application has the potential to motivate users to learn, which suggests that it is appropriate to foster sustainable agriculture strategies in the context of aquaponics.