Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based CP-ABE Scheme with Partially Hidden Access Structures

Data sharing has become a key technology to break down data silos in the big data era. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in secure data-sharing schemes to realize flexible and fine-grained access control. However, in traditional CP-ABE schemes, the access structure is directly shared along with the ciphertext, potentially leading to users’ private information leakage. Outsourcing data to a centralized third party can easily result in privacy leakage and single-point bottlenecks, and the lack of transparency in data storage and sharing casts doubts whether users’ data are safe. To address these issues, we propose a blockchain-based CP-ABE scheme with partially hidden access structures (BCP-ABE-PHAS) to achieve fine-grained access control while ensuring user privacy. First, we propose an efficient CP-ABE scheme with partially hidden access structures, where the ciphertext size is constant. To assist data decryption, we design a garbled Bloom filter to help users quickly locate the position of wildcards in the access structure. Then, to improve storage efficiency and system scalability, we propose a data storage scheme that combines blockchain technology and the interplanetary file system, ensuring data integrity. Finally, we employ smart contracts for a transparent data storage and sharing process without third-party participation. Security analysis and performance evaluation show that the proposed BCP-ABE-PHAS scheme can preserve policy privacy with efficient storage and low computational overhead.
A novel multi-objective optimization algorithm for Pareto design of a fuzzy full state feedback linearization controller applied on a ball and wheel system

The control and stabilization of a ball and wheel system around the equilibrium point are challenging tasks because it is an underactuated, nonlinear, and open-loop unstable plant. In this paper, Pareto design of a Fuzzy Full State Feedback Linearization Controller (FFSFLC) for the ball and wheel system based upon a novel multi-objective optimization algorithm is introduced. To this end, at first, a full state feedback linearization approach is employed to stabilize the dynamics of the system. Next, appropriate fuzzy systems are determined to tune the control gains. Then, a new multi-objective optimization algorithm is utilized to promote the proposed control scheme. This optimization algorithm is a combination of Simulated Annealing (SA) and Artificial Bee Colony (ABC) approaches benefiting advantages of the non-dominated Pareto solutions. To evaluate the capabilities of the suggested algorithm, its optimal solutions of several standard test functions are compared with those of five renowned multi-objective optimization algorithms. The results confirm that the proposed hybrid algorithm yields closer non-dominated Pareto solutions to the true optimal Pareto front with shorter runtimes than other algorithms. After selecting proper objective functions, multi-objective optimization of FFSFLC for the ball and wheel system is performed, and the results are compared with previous works. Simulations illustrate that the proposed strategies can accurately converge the system states to the desired conditions and yield superior robustness against disturbance signals in comparison with former studies.
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning

Vision-and-language navigation (VLN) is a challenging task that requires an agent to navigate in real-world environments by understanding natural language instructions and visual information received in real time. Prior works have implemented VLN tasks on continuous environments or physical robots, all of which use a fixed-camera configuration due to the limitations of datasets, such as 1.5-m height, 90° horizontal field of view (HFOV), and so on. However, real-life robots with different purposes have multiple camera configurations, and the huge gap in visual information makes it difficult to directly transfer the learned navigation skills between various robots. In this brief, we propose a visual perception generalization strategy based on meta-learning, which enables the agent to fast adapt to a new camera configuration. In the training phase, we first locate the generalization problem to the visual perception module and then compare two meta-learning algorithms for better generalization in seen and unseen environments. One of them uses the model-agnostic meta-learning (MAML) algorithm that requires few-shot adaptation, and the other refers to a metric-based meta-learning method with a feature-wise affine transformation (AT) layer. The experimental results on the VLN-CE dataset demonstrate that our strategy successfully adapts the learned navigation skills to new camera configurations, and the two algorithms show their advantages in seen and unseen environments respectively.
Multi‐camera video synopsis of a geographic scene based on optimal virtual viewpoint

Abstract Video synopsis offers the possibility to compress long‐term video objects into a short‐term playback. This is beneficial for the quick retrieval and expression of numerous video objects in a virtual geographic environment. However, existing methods do not consider the spatiotemporal relationships among video objects in different cameras. To overcome this problem, we propose optimal virtual viewpoint‐based multi‐camera video synopsis, which involves: locating the camera position and the field of view; constructing a video image observability model to enumerate the observable camera combinations; constructing an evaluation model to optimally select the observable combinations and obtain the virtual viewpoint group; and setting the parameters of the video object display to obtain a multi‐camera video synopsis of a geographic scene. Experimental results showed that our method achieved the rapid display of global motion across cameras with numerous video objects, and always outperformed separate camera‐based video synopsis expression.
Multi-model Deep Learning Drone Detection and Tracking in Complex Background Conditions

The recent popularity of drones with quadcopter layouts is threatening public safety and personal privacy. With the ability to hover and perform complex maneuvers even in indoor conditions, equipped with video cameras as well as capable of carrying hazardous materials, drones can truly become a security threat, especially to vulnerable organizations. Therefore, detecting and tracking drones in secured areas poses an urgent task for the surveillance system. In this paper, we design a real-time drone detection and tracking system with the combination of multiple deep learning and computer vision techniques: 1) Yolo-v4 model for detecting drones and 2) visual models for tracking drones. Besides, we have collected and labeled a larger drone dataset by mixing the existing datasets with our collected images. We evaluated three deep learning models for drone detection on this dataset and acquired the Yolo-V4 model to be the highest detection performance with AP = 34.63%. Combining this detection model and the existing visual tracking modules can boost the drone tracking up to more than 20fps for different backgrounds at around 700m by using an usual PC without GPU.
FitNet: A deep neural network driven architecture for real time posture rectification

A methodology of real time pose estimation, which is believed to mitigate many orthopaedic adversaries pertaining to wrong posture, has been illustrated in this paper. Vast array of problems get reported that are known to arise due to maintaining a wrong posture during exercising or performing yoga, for a prolonged period of time. Several developments were made with regard to this issue, yet a major drawback was the presumption that a person during exercising or performing yoga or any kind of gym sessions, will keep the camera facing only at a fixed pre-determined portrayal direction. The approach, towards this problem, mainly deals with precise ROI detection, correct identification of human body joints and tracking down the motion of the body, all in real time. A major step towards converging to the solution is determining the angular separation between the joints and comparing them with the ones desired. Another important facet of the stated methodology is analysis of performance of the deep neural architecture in different camera positions. This is a major bottleneck for many different models that are intended to track posture of a person in real time. All these operations are done efficiently, with an appropriate trade-off between time complexity and performance metrics. At the end a robust feedback based support system has been obtained, that performs significantly better than the state of the art algorithm due to the precise transformation of input color space, contributing significantly in the field of orthopaedics by providing a feasible solution to avoid body strain and unnecessary pressure on joints during exercise.
Causal BERT: Improving object detection by searching for challenging groups

Autonomous vehicles (AV) often rely on perception modules built upon neural networks for object detection. These modules frequently have low expected error overall but high error on unknown groups due to biases inherent in the training process. When these errors cause vehicle failure, manufacturers pay humans to comb through the associated images and label what group they are from. Data from that group is then collected, annotated, and added to the training set before retraining the model to fix the issue. In other words, group errors are found and addressed in hindsight. Our main contribution is a method to find such groups in foresight, leveraging advances in simulation as well as masked language modeling in order to perform causal interventions on simulated driving scenes. We then use the found groups to improve detection, exemplified by Diamondback bikes, whose performance we improve by 30 AP points. Such a solution is of high priority because it would greatly improve the robustness and safety of AV systems. Our second contribution is the tooling to run interventions, which will benefit the causal community tremendously.
Fast Hand Detection in Collaborative Learning Environments

Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related example, we study hand detection over long video recordings in collaborative learning environments. More specifically, we develop long-term hand detection methods that can deal with partial occlusions and dramatic changes in appearance.
Efficient Searchable Symmetric Encryption for Join Queries

The Oblivious Cross-Tags (OXT) protocol due to Cash et al. (CRYPTO’13) is a highly scalable searchable symmetric encryption (SSE) scheme that allows fast processing of conjunctive and more general Boolean queries over encrypted relational databases. A longstanding open question has been to extend OXT to also support queries over joins of tables without pre-computing the joins. In this paper, we solve this open question without compromising on the nice properties of OXT with respect to both security and efficiency. We propose Join Cross-Tags (JXT) - a purely symmetric-key solution that supports efficient conjunctive queries over (equi-) joins of encrypted tables without any pre-computation at setup. The JXT scheme is fully compatible with OXT, and can be used in conjunction with OXT to support a wide class of SQL queries directly over encrypted relational databases. JXT incurs a storage cost (over OXT) of a factor equal to the number of potential join-attributes in a table, which is usually compensated by the fact that JXT is a fully symmetric-key solution (as opposed to OXT which relies on discrete-log hard groups). We prove the (adaptive) simulation-based security of JXT with respect to a rigorously defined leakage profile.
Augmented Reality Interface for Sailing Navigation: a User Study for Wind Representation

This paper presents a novel Augmented Reality (AR) interface for Head Mounted Display HMD, specifically for sailing navigation. Compared to literature and the commercial solutions available, the novelty is to use boat-referenced 3D graphics. It allows representing wind direction and intensity, heading with monochrome green elements. Furthermore, we carried out a user validation study. We implemented a virtual simulator including a sailboat, the marine environment (i.e., sea, sky, marine traffic, and sounds), and the presented interface as AR overlay. We evaluated the effectiveness of the wind representation of the AR interface through an online questionnaire based on a video simulation and asking the user to imagine it as the result of an AR visualization. We defined one test scenario with wind variations and a distracting element (i.e., a crossing vessel). 75 sailors (59% experts, with more than 50 sailing days per year) participated in the questionnaire, and most of them (63%) considered the video effective at simulating the AR interface. In addition, 75% are ready to wear an AR device during sailing. Also, the usability (SUS questionnaire) and the user experience (UEQ) results provided positive results.
Joint intent detection and slot filling with wheel-graph attention networks

Intent detection and slot filling are recognized as two very important tasks in a spoken language understanding (SLU) system. In order to model these two tasks at the same time, many joint models based on deep neural networks have been proposed recently and archived excellent results. In addition, graph neural network has made good achievements in the field of vision. Therefore, we combine these two advantages and propose a new joint model with a wheel-graph attention network (Wheel-GAT), which is able to model interrelated connections directly for single intent detection and slot filling. To construct a graph structure for utterances, we create intent nodes, slot nodes, and directed edges. Intent nodes can provide utterance-level semantic information for slot filling, while slot nodes can also provide local keyword information for intent detection. The two tasks promote each other and carry out end-to-end training at the same time. Experiments show that our proposed approach is superior to multiple baselines on ATIS and SNIPS datasets. Besides, we also demonstrate that using bi-directional encoder representation from transformer (BERT) model further boosts the performance of the SLU task.
BIOCAD: Bio-Inspired Optimization for Classification and Anomaly Detection in Digital Healthcare Systems

The modern smart digital healthcare system (SDHS) is leaning towards automation of patient disease monitoring and treatment with the advent of wireless body sensor networks (WBSN) and the internet of medical things (IoMT). However, the open communication network for sensitive medical data transfer is giving rise to vulnerabilities and security concerns. To prevent adversarial manipulation of sensor measurements, SDHS IoMT controllers leverage anomaly detection systems on top of the disease classification systems. Machine learning (ML) is one of the most effective techniques for providing experience-based automated decision-making models. These models generalize well to produce the expected output for the unseen inputs from the learned patterns. Therefore, ML-based models are currently being adopted to automate the anomaly detection and disease classification tasks of SDHS. In this work, we consider a SDHS that uses supervised ML models for patient status/disease classification and unsupervised ML models for anomaly detection. However, the performance of the ML models largely depends on hyper-parameter tuning. Finding the optimal hyper-parameter is a challenging task, and it becomes more difficult and time-consuming in high-dimensional feature space. In this work, we propose BIOCAD, a comprehensive bio-inspired optimization framework for SDHS data classification and anomaly detection. The framework leverages a novel fitness function for unsu-pervised anomaly detection ML models. We experiment with state-of-the-art datasets - the Pima Indians diabetes dataset, the Parkinson dataset, and the University of Queensland vital signs (UQVS) dataset for validating our proposed strategy.
Single Image Dehazing with Dark Channel Prior

In applications related to computation of visuals, the clear picture is very crucial component but certain atmospheric conditions like haze and fog prompt the suspended particles which lead to degradation of the image. This hinders the flow of vision systems. Therefore, implementation of image dehazing has become important to improve the throughput and effectiveness of such vision systems. This paper sheds light on execution of dark channel to delete haze from single image. Different measures to optimize this algorithm are discussed with results obtained from certain successful attempts to implement this algorithm.
Towards Tight Adaptive Security of Non-interactive Key Exchange

We investigate the quality of security reductions for non-interactive key exchange (NIKE) schemes. Unlike for many other cryptographic building blocks (like public-key encryption, signatures, or zero-knowledge proofs), all known NIKE security reductions to date are non-tight, i.e., lose a factor of at least the number of users in the system. In that sense, NIKE forms a particularly elusive target for tight security reductions.
Cloud security audit – issues and challenges

This paper analyzes the cyber security audit program of services, infrastructure and processes offered through the cloud computing technology. The first part of article presents the importance of performing the process of audit, general concepts of information security audit as well as the limitations of traditional methods for auditing complex systems, such as the cloud computing. In the second part there are presented frameworks for audit planning, that can be used for every cloud model.
A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations

In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user’s interaction history and captures the user’s preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music.
Online Interval Type-2 Fuzzy Extreme Learning Machine applied to 3D path following for Remotely Operated Underwater Vehicles

In marine missions that involve 3D path following tasks, the overall goal of Underwater Vehicles (UVs) is the successful completion of a path previously specified by the operator. This implies that the path must be followed by the UV as closely as possible and arrive at a location for collection by a vessel. In this paper, an Online Interval Type-2 Fuzzy Extreme Learning Machine (OIT2-FELM) is suggested to achieve a robust following behaviour along a predefined 3D path using a Remotely Operated Underwater Vehicle (ROV). The proposed machine is a fast sequential learning scheme to the training of a more generalised model of TSK Interval Type-2 Fuzzy Inference Systems (TSK IT2 FISs) equivalent to Single Layer Feedforward Neural Networks (SLFNs). Learning new input data in the OIT2-FELM can be done one-by-one or chunk-by-chunk with a fixed or varying size. The OIT2-FELM is implemented in a hierarchical navigation strategy (HNS) as the main guidance mechanism to infer local control motions and to provide the ROV with the necessary autonomy to complete a predefined 3D path. For local path-planning, the OIT2-FELM performs signal classification for obstacle avoidance and target detection based on data collected by an on-board scan sonar. To evaluate the performance of the proposed OIT2-FELM, two different experiments are suggested. First, a number of benchmark problems in the field of non-linear system identification, regression and classification problems are used. Secondly, a number of experiments to the completion of a predefined 3D path using an ROV is implemented. Compared to other fuzzy strategies, the OIT2-FELM offered two significant capabilities. On the one hand, the OIT2-FELM provides a better treatment of uncertainty and noisy signals in underwater environments while improving the ROV’s performance. Secondly, online learning in OIT2-FELM allows continuous knowledge discovery from survey data to infer the surroundings of the ROV. Experiment results to the completion of 3D paths show the effectiveness of the proposed approach to handle uncertainty and produce reasonable classification predictions (∼90.5% accuracy in testing data).
ATMChain: Blockchain-Based Solution to Security Problems in Air Traffic Management

The air traffic management (ATM) system is an intelligent system that integrates the ground computer network, the airborne network and the space satellite (communication and navigation) network. It is connected by the ground-air data link system. Due to the openness and wide-area distribution, ATM system has many security risks, facing increasingly serious security threats, which may have an adverse impact on flight safety. In order to solve the potential cybersecurity risks in the ATM system, this paper presents an information security framework based on blockchain technology, called air traffic management blockchain (ATMChain). The ATMChain alliance blockchain network is composed of air traffic control departments, ground airport departments and System Wide Information Management centers, with specially designed block storage protocols and block structures. Through the use of a unique consensus mechanism for the civil aviation transport environment, it can achieve multicenter, secure, efficient and traceable ATM data sharing and data management. This paper presents security protection functions such as access control, data protection, identity authentication in ATMChain, and gives the protocol flow and system models diagram of these functions implementations. Finally, the analysis of the system is given. The scheme in this paper may help to improve the security and reliability of air traffic management business, and provide reference to the design and research of the fourth-generation air traffic management network.