Reviewerly/exHarmony huggingface dataset:
Deep Neural Network Perception Models and Robust Autonomous Driving Systems: Practical Solutions for Mitigation and Improvement

The National Highway Traffic Safety Administration reported that more than 90% of in-road accidents in 2015 occurred purely because of drivers? errors and misjudgments, with such factors as fatigue and other sorts of distractions being the main cause of these accidents [1]. One promising solution for reducing (or even resolving) such human errors is via autonomous or computer-assisted driving systems. Autonomous vehicles (AVs) are currently being designed with the aim of reducing fatalities in accidents by being insusceptible to typical driver errors. Moreover, in addition to improved safety, autonomous systems offer many other potential benefits to society: 1) improved fuel efficiency beyond that of human driving, making driving more cost beneficial and environmentally friendly, 2) reduced commute times due to improved driving behaviors and coordination among AVs, and 3) a better driving experience for individuals with disabilities, to name a few.
Analysis of Agricultural Supply Chain Management for Traceability of Food Products using Blockchain-Ethereum Technology

The current agricultural supply chain is a centralized system that has many issues related to integrity, tracking, organizing the transactions. There is lack of trust and transparency in the supply chain. There are many irregularities in the implementation of schemes. The consumers doubt the quality of the food supplied to them. The farmers, wholesalers are affected from the payment frauds by the middlemen. Blockchain helps us solve these issues by keeping track of all the different stages of food. The public blockchain being transparent, open, immutable, trackable helps in reducing the irregularities, frauds. This paper proposes the agricultural supply chain system model that uses the Ethereum platform. The smart contracts are developed for different stages of the supply chain. These contracts ensure that all the pre-decided conditions are satisfied before proceeding with the transactions. This system ensures security, reliability, trust, openness. It eases the transactions, administrative processes. It gives a fair chance to farmers to quote a price using smart contracts.
Auto-tune POIs: Estimation of distribution algorithms for efficient side-channel analysis

Due to the constant increase and versatility of IoT devices that should keep sensitive information private, Side-channel Analysis (SCA) attacks on embedded devices are gaining visibility in the industrial field. The integration and validation of countermeasures against SCA can be an expensive and cumbersome process, especially for the less experienced ones. Additionally, current certification procedures require to attack the devices under test using multiple SCA techniques and attack vectors, often implying a high degree of complexity. The goal of this paper is to introduce a new automatic way to ease one of the most crucial and tedious steps of profiling attacks i.e. the points of interest (POI) selection and hence assist the SCA evaluation process. To this end, we propose the usage of Estimation of Distribution Algorithms (EDAs) in the SCA field to perform the point of interest selection step together with the profiling and key recovery steps. This contribution allows an automated optimization of the attack, avoiding the need to manually perform various types of analyses with different POI combinations. We showcase our approach on several experimental use cases, including attacks on unprotected and protected AES implementations. In addition, we introduce a new public dataset containing power traces taken from distinct copies of the same device and perform attacks over this dataset using our method, dismissing in this way the portability issue.
<i>TVG-Streaming</i>: Learning User Behaviors for QoE-Optimized 360-Degree Video Streaming

360-degree video streaming shows great potential to revolutionize the streaming market, by providing much better immersive experience than standard video streams. However, its wide adoption is hindered by the surging demand of network bandwidth due to multi-screen video transmission. To reduce the bandwidth cost, one promising approach is to predict a user’s field of view (FoV), and then prefetch video tiles that a user will view a few seconds ahead. The challenge lies in that user behaviors cannot be properly captured with very limited information, especially the viewing time spent on each tile and the FoV switching behavior are hard to predict. In this paper, we propose a novel 360-degree video streaming algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TVG-Streaming</i> to optimize user experiences by learning user view behaviors. Different from previous approaches, our idea is to exploit tile-view graphs (TVGs) generated by real user behaviors and accurately estimate the probability that each tile falls in the FoV. With the tile view probability, we can determine the bitrate of each tile for delivery and buffering with limited bandwidth budget so as to maximize users’ quality of experience (QoE). For evaluation, we conduct extensive experiments using real traces and the results show that our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TVG-Streaming</i> algorithm significantly outperforms other algorithms by at least 20% improvement in terms of users’ QoE.
Nonsparse Learning with Latent Variables

A New Nonsparse Learning Methodology for High-Dimensional Data Analysis Is Coming
A highly efficient chain code for compression using an agent-based modeling simulation of territories in biological beavers

The accelerated developments in technology led to a tremendous increase in the volumes of data to be transferred and exchanged between various network channels. These advancements create a huge demand for researchers to investigate new data compression techniques. Recent evidence from the literature shows that agent-based modeling is a promising direction to reduce the size of the data and change its original representation. In this article, the objective is to build an agent-based modeling simulation for chain coding and take advantage of it in data compression. Our agent-based model is inspired by the concept of defended territories of biological beavers. To this end, we use the pixel distribution in a bi-level image to construct a virtual environment of agents, add the beavers, and build territories around them. The main idea of defended beaver territories is to allow each beaver to maintain its area and protects it from intruders. To put it another way, defended territories allow beavers to work on different parts of an image while the algorithm tracks and records their movements, as well as manages disputes between them. Our research findings represent a further step towards employing the generated codes of movements in image processing operations other than coding and compression. Additionally, the experimental results showed that the current model was prosperous, and it could outperform many existing image compression techniques, including JBIG family methods. What’s more, paired-samples t-tests reveal that the mean differences between the outcomes of the current approach and each of the other standardized benchmarks we employed in comparison are statistically significant.
How Relevant Are Selectional Preferences for Transformer-based Language Models?

Selectional preference is defined as the tendency of a predicate to favour particular arguments within a certain linguistic context, and likewise, reject others that result in conflicting or implausible meanings.The stellar success of contextual word embedding models such as BERT in NLP tasks has led many to question whether these models have learned linguistic information, but up till now, most research has focused on syntactic information.We investigate whether BERT contains information on the selectional preferences of words, by examining the probability it assigns to the dependent word given the presence of a head word in a sentence.We are using word pairs of head-dependent words in five different syntactic relations from the SP-10K corpus of selectional preference (Zhang et al., 2019b), in sentences from the ukWaC corpus, and we are calculating the correlation of the plausibility score (from SP-10K) and the model probabilities.Our results show that overall, there is no strong positive or negative correlation in any syntactic relation, but we do find that certain head words have a strong correlation, and that masking all words but the head word yields the most positive correlations in most scenarios-which indicates that the semantics of the predicate is indeed an integral and influential factor for the selection of the argument.
Local Path Planning Using Artificial Potential Field for Waypoint Tracking with Collision Avoidance

In this paper, we propose a waypoint tracking scheme with a collision-avoidance system using the artificial potential field (APF) with speed planning. It is shown that there is little literature about the combination work of waypoint tracking and APF-based local path planner. We present a new path planning method to smoothly join the local path predicted by APF to avoid obstacles and the path given by irregular waypoints to eliminate severe yawing of the heading angle of the vehicle. The proposed speed planning combined with APF effectively prevents possible excessive tire slip angle of the vehicle driving at high speed. The simulation results show that the proposed path planning algorithm with speed planning is effective in collision avoidance with static and/or dynamic obstacles for the waypoint tracking scenario. This proposed method is validated with the vehicle dynamic lateral motion model in MATLAB/Simulink and CarSim.
MR-Block: A Blockchain-Assisted Secure Content Sharing Scheme for Multi-User Mixed-Reality Applications in Internet of Military Things

Internet of Things (IoT) established the foundation of the smart world via its diverse functionalities (e.g., remote data acquisition, automated task execution). This trend continues to military applications too by introducing internet of military things (IoMT). Due to being at infancy level, new technologies require to move forward and assist IoMT to gain maturity. Mixed reality (MR) can be a potential technology which fuses the virtual and actual world. MR can improve the quality of service (QoS) in terms of inventory management, remote mission handling, battlefield assistance, etc. However, data among these applications is surrounded by cyber threats (e.g., illegal data modification, unauthorised data access). Blockchain is another promising technology which brings security in the distributed world. A content sharing scheme for MR application is proposed on the top of blockchain to bring security in the multi-user environment in IoMT. A smart contract is employed to manage security in accessing data by different users. Moreover, an experimental environment is set to observe the performance of the proposed scheme. The analysis manifests that proposed scheme maintains security without affecting the regular performance.
Exploring the Current and Future States of Augmented Reality in the Construction Industry

In construction, adoption of advanced technologies has the potential to significantly improve the performance of projects. The construction industry has experienced a radical evolution as it entered the era of digital documentation and information exchange. Construction design began with drafting boards, then moved to Computer-Aided-Design, and finally to Building Information Modeling. At each stop along that journey, gains were made in information density and exchange. However, for all the progress made thus far, the paradox of designing the 3D in 2D space remains. As Industry 4.0 continues to evolve, it is imperative that construction firms seek, find, and adopt new technologies—both to remain competitive and to grow in the industry. Augmented reality (AR), a pillar of Industry 4.0, has the potential to transform the construction industry. This paper explores the current and potential future states of AR in construction. Might this technology gain momentum and take hold in construction, as it has done in other industries? To investigate this question, industry practitioners were surveyed. 128 responses were collected and analyzed to provide insight into the current and potential future states of AR in construction.
The Postdigital University: Do We Still Need Just a Little of That Human Touch?

An increasing body of literature considers the role of belonging and social connectivity in undergraduate student success. The core tenet of this research is that relationships are crucial to the development of a sense of belonging. However, within the Higher Education (HE) sector, our processes, and therefore how we interact with students, are becoming more and more automated. None more so than during the Covid-19 pandemic and the ‘new normal’ in HE. This paper considers how we, as a profession, might support each student’s developing sense of belonging within a sector that is shifting towards increased digitalisation. This is achieved through considering the political agenda that drives the creation of digital education and some of the assumptions that underpin the movement towards it. As a result, a theoretical platform is created to consider the areas where digitisation impacts on teaching staff, and on students, and how this relates to each student’s sense of belonging within HE. The inclusion of two case studies has provided the opportunity to answer two key questions: (1) What is important to students developing a personal sense of belonging in HE during their first few weeks in a university? (2) How can the differentiated human touch be provided by ‘third space’ professionals both in person and virtually?
Unintentional forking analysis in wireless blockchain networks

The forking problem plays a key role in the security issue, which is a major concern in the blockchain system. Although many works studied the attack strategy, consensus mechanism, privacy-protecting and security performance analysis, most of them only address the intentional forking caused by a malicious attacker. In fact, without any attacker, unintentional forking still remains due to transmission delay and failure, especially in wireless network scenarios. To this end, this paper investigates the reason for generating unintentional forking and derives the forking probability expression in Wireless Blockchain Networks (WBN). Furthermore, in order to illustrate the unintentional forking on the blockchain system, the performances in terms of resource utilization rate, block generation time, and Transaction Per Second (TPS) are investigated. The numerical results show that the target difficulty of hash algorithm in generating a new block, the delay time of broadcasting, the network scale, and the transmission failure probability would affect the unintentional forking probability significantly, which can provide a reliable basis for avoiding forking to save resource consumption and improving system performance.
Boundary-Aware Supervoxel-Level Iteratively Refined Interactive 3D Image Segmentation With Multi-Agent Reinforcement Learning

Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints. While iterative in nature, most existing interactive segmentation methods tend to ignore the dynamics of successive interactions and take each interaction independently. We here propose to model iterative interactive image segmentation with a Markov decision process (MDP) and solve it with reinforcement learning (RL) where each voxel is treated as an agent. Considering the large exploration space for voxel-wise prediction and the dependence among neighboring voxels for the segmentation tasks, multi-agent reinforcement learning is adopted, where the voxel-level policy is shared among agents. Considering that boundary voxels are more important for segmentation, we further introduce a boundary-aware reward, which consists of a global reward in the form of relative cross-entropy gain, to update the policy in a constrained direction, and a boundary reward in the form of relative weight, to emphasize the correctness of boundary predictions. To combine the advantages of different types of interactions, i. e., simple and efficient for point-clicking, and stable and robust for scribbles, we propose a supervoxel-clicking based interaction design. Experimental results on four benchmark datasets have shown that the proposed method significantly outperforms the state-of-the-arts, with the advantage of fewer interactions, higher accuracy, and enhanced robustness.
U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.
An integrated framework of learning and evidential reasoning for user profiling using short texts

Inferring user profiles based on texts created by users on social networks has a variety of applications in recommender systems such as job offering, item recommendation, and targeted advertisement. The problem becomes more challenging when working with short texts like tweets on Twitter, or posts on Facebook. This work aims at proposing an integrated framework based on Dempster–Shafer theory of evidence, word embedding, and k-means clustering for user profiling problem, which is capable of not only working well with short texts but also dealing with uncertainty inherently in user texts. The proposed framework is essentially composed of three phases: (1) Learning abstract concepts at multiple levels of abstraction from user corpora; (2) Evidential inference and combination for user modeling; and (3) User profile extraction. Particularly, in the first phase, a word embedding technique is used to convert preprocessed texts into vectors which capture semantics of words in user corpus, and then k-means clustering is utilized for learning abstract concepts at multiple levels of abstraction, each of which reflects appropriate semantics of user profiles. In the second phase, by considering each document in user corpus as an evidential source that carries some partial information for inferring user profiles, we first infer a mass function associated with each user document by maximum a posterior estimation, and then apply Dempster’s rule of combination for fusing all documents’ mass functions into an overall one for the user corpus. Finally, in the third phase, we apply the so-called pignistic probability principle to extract top-n keywords from user’s overall mass function to define the user profile. Thanks to the ability of combining pieces of information from many documents, the proposed framework is flexible enough to be scaled when input data coming from not only multiple modes but different sources on web environments. Besides, the resulting profiles are interpretable, visualizable, and compatible in practical applications. The effectiveness of the proposed framework is validated by experimental studies conducted on datasets crawled from Twitter and Facebook.
A Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces

Determining the roughness of technical surfaces is an important task in many engineering disciplines. In civil engineering, for instance, the repair and reinforcement of building component parts (such as concrete structures) requires a certain surface roughness in order to ensure the bond between a coating material and base concrete. The sand patch method is so far the state-of-the-art for the roughness measurement of concrete structures. Although the method is easy to perform, it suffers from considerable drawbacks. Consequently, more sophisticated measurement systems are required. In a research project, we developed a novel camera-based alternative, which comes with several advantages. The measurement system consists of a mechanical cross slide that guides an industrial camera over a surface to be measured. Images taken by the camera are used for 3D reconstruction. Finally, the reconstructed point clouds are used to estimate roughness. In this article, we present our measurement system (including the hardware and the self-developed software for 3D reconstruction). We further provide experiments to camera calibration and evaluation of our system on concrete specimens. The resulting roughness estimates for the concrete specimens show a strong linear correlation to reference values obtained by the sand patch method.
Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system

Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML.We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes.The best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic shock (0.857), XGBoost for intensive care unit admission (0.902), and logistic regression for in-hospital mortality (0.889) in the testing data. The predictive model was further applied in the hospital information system to assist physicians' decisions in real time.ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.
Human Action Recognition Using Deep Learning Methods

The goal of human action recognition is to identify and understand the actions of people in videos and export corresponding tags. In addition to spatial correlation existing in 2D images, actions in a video also own the attributes in temporal domain. Due to the complexity of human actions, e.g., the changes of perspectives, background noises, and others will affect the recognition. In order to solve these thorny problems, three algorithms are designed and implemented in this paper. Based on convolutional neural networks (CNN), Two-Stream CNN, CNN+LSTM, and 3D CNN are harnessed to identify human actions in videos. Each algorithm is explicated and analyzed on details. HMDB-51 dataset is applied to test these algorithms and gain the best results. Experimental results showcase that the three methods have effectively identified human actions given a video, the best algorithm thus is selected.