Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Control of Video Transmissions for Drone-based Intelligent Transportation Systems

The drones are now frequently used for many smart city applications including intelligent transportation to provide situational awareness for drivers as well as other stakeholders that manage the traffic. In such situations one of the widely collected data is video that is recorded by a drone and streamed in real-time to a remote control center. The data can then be accessed through cloud services to do further analysis and take actions. However, this captured video may contain private information from the passing by citizens and allow recognition and tracking if it is intercepted by malicious users. While the video data can be stored as encrypted in the cloud, this still does not address the privacy problem as the third party providers still need to decrypt the data to perform any further processing. To address this issue, we propose using fully homomorphic encryption (FHE)which will not only provide confidentiality of the data but also enable processing on the encrypted video data by cloud providers and other third parties without exposing any privacy. However, since fully homomorphic systems have a lot of overhead, in this paper, we propose to conduct background extraction on video images and transmit only the changing foreground to minimize data transmission. As we use FHE, this allows reconstruction of the video at the server without decrypting the data. We tested the feasibility of the proposed approach extensively under various conditions including the type of FHE used, the underlying communication protocols and video size. The results indicate that our approach can even outperform AES-based method in terms of total time to complete the video transmission while additionally enabling privacy features.
Chunk Duration--Aware SDN-Assisted DASH

Although Dynamic Adaptive Streaming over HTTP (DASH) is the pillar of multimedia content delivery mechanisms, its purely client-based adaptive video bitrate mechanisms have quality-of-experience fairness and stability problems in the existence of multiple DASH clients and highly fluctuating background traffic on the same shared bottleneck link. Varying chunk duration among different titles of multiple video providers exacerbates this problem. With the help of the global network view provided by the software-defined networking paradigm, we propose a centralized joint optimization module-assisted adaptive video bitrate mechanism that takes diversity of chunk sizes among different content into account. Our system collects possible video bitrate levels and chunk duration from DASH clients and simply calculates the optimal video bitrates per client based on the available capacity and chunk duration of each client’s selected content while not invading users’ privacy. By continuously following the background traffic flows, it asynchronously updates the target video bitrate levels to avoid both buffer stall events and network underutilization issues rather than bandwidth slicing, which brings about scalability problems in practice. It also guarantees fair startup delays for video sessions with various chunk duration. Our experiments clearly show that our proposed approach considering diversity of chunk duration and that background traffic fluctuations can significantly provide a better and fair quality of experience in terms of structural similarity--based video quality and startup delay compared to both purely client-based and state-of-the-art software-defined networking--based adaptive bitrate mechanisms.
Object Detection-Based Location and Activity Classification from Egocentric Videos: A Systematic Analysis

Egocentric vision has emerged in the daily practice of application domains such as lifelogging, activity monitoring, robot navigation and the analysis of social interactions. Plenty of research focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that indoor locations and daily activities can be characterized by the presence of specific objects. Objects can be obtained either from laborious human annotations or automatically, using vision-based detectors. We perform a study regarding the use of object detections as input for location and activity classification and analyze the influence of various detection parameters. We compare our detections against manually provided object labels and show that location classification is affected by detection quality and quantity. Utilization of the temporal structure in object detections mitigates the consequences of noisy ones. Moreover, we determine that the recognition of activities is related to the presence of specific objects and that the lack of explicit associations between certain activities and objects hurts classification performance for these activities. Finally, we discuss the outcomes of each task and our method's potential for real-world applications.
In-Bed Pressure-Based Pose Estimation Using Image Space Representation Learning

Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equip-ping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data for subsequent pose estimation. Our model transforms the ambiguous pressure maps to images containing shapes and structures similar to the common input domain of the pre-existing pose estimation methods. As a result, we show that our model is able to reconstruct unclear body parts, which in turn enables pose estimators to accurately and robustly estimate the pose. We train and test our method on a manually annotated public pressure map dataset using a combination of loss functions. Results confirm the effectiveness of our method by the high visual quality in the generated images and the high pose estimation rates achieved.
Opponent Aware Reinforcement Learning

We introduce Threatened Markov Decision Processes (TMDPs) as an extension of the classical Markov Decision Process framework for Reinforcement Learning (RL). TMDPs allow suporting a decision maker against potential opponents in a RL context. We also propose a level-k thinking scheme resulting in a novel learning approach to deal with TMDPs. After introducing our framework and deriving theoretical results, relevant empirical evidence is given via extensive experiments, showing the benefits of accounting for adversaries in RL while the agent learns
ASSESSMENT OF STREAM SEDIMENTS POLLUTION BY POTENTIALLY TOXIC ELEMENTS IN THE ACTIVE MINING AREA OF OKPELLA, EDO STATE, NIGERIA

An active gold mining area in Okpella, Edo State, Nigeria was studied to assess the contribution of gold mining to the concentrations of potentially toxic elements (PTEs) in stream sediments. Standard geochemical sampling and sample treatment techniques were employed, and samples were analysed using the energy dispersive X-ray fluorescence (XRF) method. The concentrations of arsenic (As), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb) and zinc (Zn) were determined in fifteen stream sediment samples from an active gold mining area, which also  receives discharged marble mine water. The enrichment factor (EF) and geoaccumulation index (Igeo) were calculated using the XRF analysis results to assess the level of PTE pollution in the area. The sediments showed a PTE concentration trend of Cu > Zn > Pb > As > Cr > Hg > Ni > Co. The EF results revealed extremely severe enrichment of Hg, moderate enrichment of Cu, minor enrichment of As and Pb, and no enrichment of Co, Cr, Ni and Zn in the sediments. The Igeo also showed that the sediments were extremely polluted with Hg, moderately polluted with Cu but there was no evidence of pollution from other PTEs. Extreme pollution of the sediments by Hg and its enrichments in Cu, As and Pb are due to indiscriminate active artisanal gold mining in the area. It is recommended that immediate remediation measures should be enforced to mitigate the possible environmental health hazards to humans and livestock in the area.
Model Vulnerability to Distributional Shifts over Image Transformation Sets

We are concerned with the vulnerability of computer vision models to distributional shifts. We cast this problem in terms of combinatorial optimization, evaluating the regions in the input space where a (black-box) model is more vulnerable. This is carried out by combining image transformations from a given set and standard search algorithms. We embed this idea in a training procedure, where we define new data augmentation rules over iterations, accordingly to the image transformations that the current model is most vulnerable to. An empirical evaluation on classification and semantic segmentation problems suggests that the devised algorithm allows to train models more robust against content-preserving image transformations, and in general, against distributional shifts.
Modeling a Microgrid Using Fuzzy Cognitive Maps

The energy problem is among the most important issues in the global community over the last decades. Worldwide researchers have focused their attention and work to the increased use of renewable energy sources as a solution to the greenhouse effect. The reduction of the emitted pollutants, as well as managing, controlling and saving energy, are key research items. This paper attempts to cover part of the load of the studied microgrid, which consists of three buildings of the University of Patras using the method of Fuzzy Cognitive Maps. The goal is using renewable energy sources to cover 20% of their total load, aiming to decongest the network at peak times.
Forecasting the Impact of Information Security Breaches on Stock Market Returns and VaR Backtest

This paper wants to analyse the cyber-risk impact on economy in particular on the returns of the companies suffering information braches.The problem has become very interesting in recent years in the literature for the large dependence of the business with cyber world.The analysis focuses on event study in which the impact of cyber-attacks on stock prices of selected companies is investigated.Cyber-risk phenomenon is processed considering a portfolio of targeted assets, in order to analyse their correlation.Risk measures, such as VaR, will be evaluated and backtested using different methods to monitor which one is able to better capture this type of riskiness.
Construction and Performance Analysis of Image Steganography-based Botnet in KakaoTalk Openchat

Once a botnet is constructed over the network, a bot master and bots start communicating by periodically exchanging messages, which is known as botnet C&amp;C communication, in order to send botnet commands to bots, collect critical information stored in bots, upgrade software functions of malwares installed in bots, and so on. For this reason, most existing botnet detection techniques focus on monitoring and capturing suspicious communications between the bot master and bots. Meanwhile, botnets continue to evolve to hide their C&amp;C communication. Recently, a novel type of botnet using image steganography techniques and SNS (Social Network Service) platforms, which is known as image steganography-based botnet or stegobotnet, has emerged to make its C&amp;C communications undetectable by existing botnet detection systems. In stegobotnets, image files used in SNSs carry messages (between the bot master and bots) which are hidden in them by using image steganography techniques. In this paper, we first investigate whether major SNS platforms such as KakaoTalk, Facebook, and Twitter can be suitable for constructing image steganography-based botnets. Next, we construct a part of stegobotnet based on KakaoTalk, and conduct extensive experiments including digital forensic analysis (1) to validate stegobotnet C&amp;C communication can be successful in KakaoTalk and (2) to examine its performance in terms of C&amp;C communication reliability.
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms

We study the convergence of a class of gradient-based Model-Agnostic Meta-Learning (MAML) methods and characterize their overall complexity as well as their best achievable accuracy in terms of gradient norm for nonconvex loss functions. We start with the MAML method and its first-order approximation (FO-MAML) and highlight the challenges that emerge in their analysis. By overcoming these challenges not only we provide the first theoretical guarantees for MAML and FO-MAML in nonconvex settings, but also we answer some of the unanswered questions for the implementation of these algorithms including how to choose their learning rate and the batch size for both tasks and datasets corresponding to tasks. In particular, we show that MAML can find an $ε$-first-order stationary point ($ε$-FOSP) for any positive $ε$ after at most $\mathcal{O}(1/ε^2)$ iterations at the expense of requiring second-order information. We also show that FO-MAML which ignores the second-order information required in the update of MAML cannot achieve any small desired level of accuracy, i.e., FO-MAML cannot find an $ε$-FOSP for any $ε&gt;0$. We further propose a new variant of the MAML algorithm called Hessian-free MAML which preserves all theoretical guarantees of MAML, without requiring access to second-order information.
Application of Extractive Text Summarization Algorithms to Speech-to-Text Media

This paper presents how speech-to-text summarization can be performed using extractive text summarization algorithms. Our objective is to make a recommendation about which of the six text summary algorithms evaluated in the study is the most suitable for the task of audio summarization. First, we have selected six text summarization algorithms: Luhn, TextRank, LexRank, LSA, SumBasic, and KLSum. Then, we have evaluated them on two datasets, DUC2001 and OWIDSum, with six ROUGE metrics. After that, we have selected five speech documents from ISCI Corpus dataset, and we have transcribed using the Automatic Speech Recognition (ASR) from Google Cloud Speech API. Finally, we applied the studied extractive summarization algorithms to these five text samples to obtain a text summary from the original audio file. Experimental results showed that Luhn and TextRank obtained the best performance for the task of extractive speech-to-text summarization on the samples evaluated.
Lateral State Estimation of Preceding Target Vehicle Based on Multiple Neural Network Ensemble

Preceding target vehicle (PTV) motion recognition play a pivotal role in autonomous vehicles. Motion states such as yaw rate, longitudinal and lateral velocity are critical for ego vehicle decision-making and control. However, lateral states of a PTV can hardly be measured directly by common onboard sensors and the PTV lateral state estimation has been seldom addressed in existing literatures. In this paper, a novel estimation scheme based on multiple neural network ensemble is proposed for PTV lateral state estimation. First, PTV lateral kinematics is presented based on vehicle-road relationship and a novel PTV lateral motion model is constructed to interpret the PTV lateral motion. Then, neural network observer with the PTV lateral kinematics as prior knowledge is designed and training data are collected in simulation environment. The neural network observer is trained using Levenberg-Marquardt backpropagation with Bayesian regularization (LMBR) to improve the generalization capability. Finally, to further improve the performance of the neural network estimation method, multiple neural network observers are integrated by weighted averaging strategy. The effectiveness of proposed approach is verified through hardware-in-the-Ioop (HiL) experiments conducted in designed verification scenarios, and compared with model-based method and other three learning methods. The experiment results reveal that the proposed method outperforms other typical methods and achieves accurate estimation of the PTV lateral states.
Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.
On Single Source Robustness in Deep Fusion Models

Algorithms that fuse multiple input sources benefit from both complementary and shared information. Shared information may provide robustness against faulty or noisy inputs, which is indispensable for safety-critical applications like self-driving cars. We investigate learning fusion algorithms that are robust against noise added to a single source. We first demonstrate that robustness against single source noise is not guaranteed in a linear fusion model. Motivated by this discovery, two possible approaches are proposed to increase robustness: a carefully designed loss with corresponding training algorithms for deep fusion models, and a simple convolutional fusion layer that has a structural advantage in dealing with noise. Experimental results show that both training algorithms and our fusion layer make a deep fusion-based 3D object detector robust against noise applied to a single source, while preserving the original performance on clean data.
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions

Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems.In this study, we explore for the first time, the application of transfer learning (TRL) and multitask learning (MTL) to the identification of Multiword Expressions (MWEs).For MTL, we exploit the shared syntactic information between MWE and dependency parsing models to jointly train a single model on both tasks.We specifically predict two types of labels: MWE and dependency parse.Our neural MTL architecture utilises the supervision of dependency parsing in lower layers and predicts MWE tags in upper layers.In the TRL scenario, we overcome the scarcity of data by learning a model on a larger MWE dataset and transferring the knowledge to a resource-poor setting in another language.In both scenarios, the resulting models achieved higher performance compared to standard neural approaches.
A multimodal conversational coach for active ageing based on sentient computing and m‐health

Abstract As life expectancy increases, it has become more necessary to find ways to support healthy ageing. A number of active ageing initiatives are being developed nowadays to foster healthy habits in the population. This paper presents our contribution to these initiatives in the form of a multimodal conversational coach that acts as a coach for physical activities. The agent can be developed as an Android app running on smartphones and coupled with cheap widely available sport sensors in order to provide meaningful coaching. It can be employed to prepare exercise sessions, provide feedback during the sessions, and discuss the results after the exercise. It incorporates an affective component that informs dynamic user models to produce adaptive interaction strategies.
Top-N-Targets-Balanced Recommendation Based on Attentional Sequence-to-Sequence Learning

User's behaviors and preferences alter with the temporal evolution dynamically, which leads to low performance, such as the Hit Rate and Normalized Discounted Cumulative Gain (NDCG). Understanding the dynamics of users' behaviors and preferences can improve the performance of recommendation system. In this paper, we propose a Top-N-targets-balanced recommendation based on attentional sequence-to-sequence (Seq2Seq) learning to capture the users' transient interests. The attentional Seq2Seq learning is introduced to largely exploit the coherence of users' sequential behaviors and preferences in the out module of our sequential recommendation. We use two methods to get the Top-N outputs as inputs of the attentional Seq2Seq learning. The direct selection method is choosing the k items with high probability. The simulated generation method is an improvement of the network by simulating the output. We balance the loss between Top-N outputs and the sequence targets to train the neural networks, which include Long Short-Term Memory and attentional Seq2Seq learning. Besides, we modify the recommendation list generation method to further improve the performance. Experimental results demonstrate that our methods outperform existing algorithms including state-of-the-art NCF, ItemPop, ItemKNN, BPR, eAls, and SVD++ on the performance of HR and NDCG. In the best case, the NDCG of our proposal is 14.72% higher than that of NCF.
Outlier Detection and Robust PCA Using a Convex Measure of Innovation

This paper presents a provable and strong algorithm, termed Innovation Search (iSearch), to robust Principal Component Analysis (PCA) and outlier detection. An outlier by definition is a data point which does not participate in forming a low dimensional structure with a large number of data points in the data. In other word, an outlier carries some innovation with respect to most of the other data points. iSearch ranks the data points based on their values of innovation. A convex optimization problem is proposed whose optimal value is used as our measure of innovation. We derive analytical performance guarantees for the proposed robust PCA method under different models for the distribution of the outliers including randomly distributed outliers, clustered outliers, and linearly dependent outliers. Moreover, it is shown that iSearch provably recovers the span of the inliers when the inliers lie in a union of subspaces. In the challenging scenarios in which the outliers are close to each other or they are close to the span of the inliers, iSearch is shown to outperform most of the existing methods.