Reviewerly/exHarmony huggingface dataset:
An efficient adaptive genetic algorithm technique to improve the neural network performance with aid of adaptive GA operators

The neural network (NN) performance improvement is one of the major topics. Thus an adaptive genetic algorithm (AGA) technique is proposed by making adaptive with respect to genetic operators like crossover and mutation. Our adaptive GA technique starts with the generation of initial population as same as the normal GA and performs the fitness calculation for each individual generated chromosome. After that, the genetic operator's crossover and mutation will be performed on the best chromosomes. The AGA technique will be utilised in the NN performance improvement process. The AGA will utilise some parameters obtained from the NN by back propagation algorithm. The utilisation of NN parameters by AGA will improve the NN performance. Hence, the NN performance can be improved more effectively by achieving high performance ratio than the conventional GA with NN. The technique will be implemented in the working platform of MATLAB and the results will be analysed to demonstrate the performance of the proposed AGA.
Insider threat grows

Two-thirds of businesses worldwide believe they've suffered a breach due to misused or abused employee access in the past year, according to research by BeyondTrust. And 62% believe they've had a breach due to compromised vendor access.
Smart Contract for Decentralized Water Management System using Blockchain Technology

There is A finite amount of portable water which is decreasing day by day. Rapid degradation of useful water on earth results in an unkind impact on livelihood. In future, people may have to face (DAY 0) problem therefore, conservation of water is essential. A solution has been proposed to this problem that is “decentralized water management system” using blockchain technology. Blockchain technology can help to use water more efficiently so that every household can lend/borrow the required/extra water from its peer household in the network. In this research work, water ledger architecture has been proposed. This architecture can serve as the basis for Blockchain implementation which can help inbuilding transparency in the water management system with the ultimate goal of Water Conservation. To purpose a system architecture that meets the “demand and supply” of all consumers in a peer-to-peer network so that water can be conserved. A smart contract has been written for transactions (P2P network of 10 household) using Ethereum as a platform. A web interface is created for consumers. Hence, the overall objective is to create a smart water management system for 10 households using blockchain technology to conserve water by medium of sharing water among peer-to-peer as per their needs.
Drone forensics: investigative guide for law enforcement agencies

Nowadays, sophisticated criminals are using drones in different ways like spying, bombarding, shooting, etc. Law enforcement agencies have to deal with the biggest challenges to cope up and to investigate such incidents from the drones found from the crime scene. Drones used in crimes can provide valuable sources of information by analysing storage media and logs on drones, including flying routes and their take-off source and landing destinations as well as multimedia like images and videos taken by drones, whom have controlled and remotely operated it, the controller ID and metadata. In this research, we are going to discuss how such incidents can be investigated through forensics procedures. In which, the investigator can perform drone acquisition, evidence collection, forensic investigation, and reporting. This will cover the general legal procedure to collect and analyse any drones from the crime scene and investigate inside the lab.
Recommender Systems

When someone asks where machine learning is broadly applied in an industrial context, recommender systems is a typical answer. Indeed, these systems are ubiquitous, and we rely on them a lot. Amazon is maybe the best example of an e-commerce site that utilizes many types of recommendations to enhance users’ experience and help them quickly find what they are looking for. Spotify, whose domain is music, is another good example. Despite heavy usage of machine learning, recommender systems differ in two crucial ways from classically trained ones:
Control power of high-dimensional controlled dense coding

We quantitatively analyze and evaluate the control power in a high-dimensional noisy quantum channel for transmitting classical information. We calculate the control power of the controlled dense coding scheme in a four-dimensional extended Greenberger-Horne-Zeilinger (GHZ) class state channel. Different from controlled teleportation, there is no tradeoff between the control power and the classical capacity of the optimal four-dimensional GHZ state quantum channel. Only when the channel is noisy and the sender is allowed to gain some control authority can the tradeoff between the control power and the classical capacity be activated, and the overall control power for transmitting classical information will be enhanced by reducing the classical capacity of the high-dimensional quantum channel. This noise induced characteristic is very different from that of transmitting quantum information, and it may provide new ideas and methods to develop the resource theory of quantum channels.
Artificial intelligence application in university libraries of Pakistan: SWOT analysis and implications

Purpose The purpose of this study Artificial intelligence (AI) is one of the important emerging trends in information technology and is slowly being implemented in libraries. The researchers have presented a brief strengths, weaknesses, opportunities and threats (SWOT) analysis of the application of AI in Pakistani university libraries. Design/methodology/approach This study uses an ethnographic approach for data retrieval. Five chief librarians were interviewed by phone, during which they were asked to identify one key strength, weakness, opportunity and threat in terms of introducing AI technologies. The researchers have used a standard SWOT matrix to report the respondents’ comments. Findings AI is already slowly being introduced into Pakistani university libraries. While commenting on ways in which AI could help their libraries deliver more innovative services and better meet user needs, respondents expressed concern about the investment required in funding, time and staff. Research limitations/implications Further study is indicated to identify existing AI implementations in Pakistani university libraries and to assess relevant library users’ perspectives. This study is limited to brief, qualitative data; its main purpose is to validate the use of a SWOT analysis. Practical implications Given that AI-based tools are already being used in libraries to some degree regardless of location, now is an opportune time to develop strategies for implementing AI technologies more widely. A SWOT analysis can be used to identify and categorize challenges and risks specific to AI in a logical way to support strategic decision-making. Originality/value To date, no SWOT analysis has been conducted in the context of AI applications in libraries, let alone specifically university libraries in Pakistan.
Fused RetinaNet for small target detection in aerial images

Recently, there has been renewed interest in object detection in computer vision. Several attempts have been made to improve small objects recognition, particularly in aerial images, due to their weak representative features and low resolution. However, these instances are still a challenge. The principal objective of this paper is to introduce a Fused RetinaNet detector, an enhanced RetinaNet with a novel context fusion module instead of the feature pyramid network (FPN), to improve low layers semantic information and top layers spatial resolution. This module firstly aggregates multi-scale backbone feature maps at once to make a robust shallow layer. Then, a parallel-branch dilated module is proposed in front of each network level to present more context information. Finally, aggregation and dilated modules are laterally connected at each backbone level via a bottom-up path approach to compensate for information loss during downsampling. Comprehensive experiments are carried out on NWPU VHR-10 and DOTA aerial image datasets. The evaluation reveals the proposed Fused RetinaNet superiority in aerial images small target detection. It achieves a mean average precision (mAP) value of 91.83% and 57.13% on NWPU VHR-10 and DOTA, respectively. Also, it scores a mAP of 62.59% on DOTA when the k-means clustering algorithm is used.
Preserving Location Privacy and Accurate Task Allocation in Edge-assisted Mobile Crowdsensing

Mobile crowdsensing enables collaborative data sensing between cloud server and mobile nodes. To participate in the sensing task, mobile nodes upload their locations to the centralized cloud for task allocation. However, revealing locations to an untrusted cloud results in privacy leakage, such as trajectories tracking and home address exposal, threatening the personal security. Obfuscation and cryptography based schemes are two main solutions to protect the location privacy. However, these schemes may either degrade the accuracy of task allocation or rely on some strong assumptions. Thus, how to protect location privacy without strong assumptions while remaining high accuracy in task allocation is challenging. In this paper, we propose a secure protocol for edge-assisted mobile crowdsensing, which removes the assumption that the cloud cannot collude with mobile nodes. Specifically, we deploy homomorphic encryption among service requestor, cloud server and edge nodes in a collaborative manner. Benefiting from the additive property of the cryptosystem, the cloud is able to securely calculate the mobile node&#x2019;s travel distance while knowing nothing about the mobile mode&#x2019;s location and task location. Based on the protocol, two types of location-dependent task allocation, travel distance based task allocation and spatial distribution based task allocation, can be implemented with location privacy preservation. Experimental results show the effectiveness of our work in task allocation. In addition, comprehensive privacy discussion indicates that the proposed protocol is secure from the collusion between cloud and mobile nodes, while preserving the task location and location privacy of mobile nodes.
Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package

Ecological research, just as all Earth System Sciences, is becoming increasingly data-rich. Tools for processing of “big data” are continuously developed to meet corresponding technical and logistical challenges. However, even at smaller scales, data sets may be challenging when best practices in data exploration, quality control and reproducibility are to be met. This can occur when conventional methods, such as generating and assessing diagnostic visualizations or tables, become unfeasible due to time and practicality constraints. Interactive processing can alleviate this issue, and is increasingly utilized to ensure that large data sets are diligently handled. However, recent interactive tools rarely enable data manipulation, may not generate reproducible outputs, or are typically data/domain-specific. We developed datacleanr, an interactive tool that facilitates best practices in data exploration, quality control (e.g., outlier assessment) and flexible processing for multiple tabular data types, including time series and georeferenced data. The package is open-source, and based on the R programming language. A key functionality of datacleanr is the “reproducible recipe”—a translation of all interactive actions into R code, which can be integrated into existing analyses pipelines. This enables researchers experienced with script-based workflows to utilize the strengths of interactive processing without sacrificing their usual work style or functionalities from other (R) packages. We demonstrate the package’s utility by addressing two common issues during data analyses, namely 1) identifying problematic structures and artefacts in hierarchically nested data, and 2) preventing excessive loss of data from ‘coarse,’ code-based filtering of time series. Ultimately, with datacleanr we aim to improve researchers’ workflows and increase confidence in and reproducibility of their results.
Revisiting Loss Recovery for High-Speed Transmission

The emerging applications including cloud AR/VR gaming, ultra-high definition (UHD) streaming, Metaverse, etc. imply the demand for ultra-high bandwidth transmission in the wide area network (WAN). During the past four decades, a great number of high-speed TCP variants have been proposed to improved the throughput for WAN transmission. This paper conducts a measurement study on loss recovery for high-speed transmission, and exposes that receive buffer starvation is a result of capability mismatch between loss recovery and high-speed TCP advancements such as non-loss-based congestion control. To mitigate this mismatch problem, an opportunistic random redundant retransmission (OR3) algorithm, as well as its TCP implementation TCP-OR3, is proposed. OR3 accelerates loss recovery by minimizing the maximum retransmission times of each packet. Experiment results shows that TCP-OR3 alleviates receive buffer starvation and tackles the bottleneck of the legacy approaches such as parallel TCP in the case of high-speed transmission.
Study on automatic measurement of deformation and application by digital single-beam speckle interferometry

The purpose of the digital single-beam speckle interferometry is to extract displacement information from gray images of the specimen surface before and after deformation, which is derived primarily from the single-beam speckle interferometry. Single-beam speckle interferometry uses optical Fourier transform to extract displacement information in the frequency domain. While digital single-beam speckle interferometry uses a computer to implement optical Fourier transform after digitizing an image to extract displacement information in the frequency domain. Four phase shift images are generated by a computer and the phase shift technology is used to obtain wrapped and unwrapped fringe patterns. Furthermore, based on the fringe patterns, the direction and magnitude of the displacement can be determined automatically. The factors that may influence measurement accuracy are investigated. To validate the reliability of this method, it is used to measure displacement near the crack tip of the cracked concrete beam.
Adversarial Data Hiding in Digital Images

In recent studies of generative adversarial networks (GAN), researchers have attempted to combine adversarial perturbation with data hiding in order to protect the privacy and authenticity of the host image simultaneously. However, most of the studied approaches can only achieve adversarial perturbation through a visible watermark; the quality of the host image is low, and the concealment of data hiding cannot be achieved. In this work, we propose a true data hiding method with adversarial effect for generating high-quality covers. Based on GAN, the data hiding area is selected precisely by limiting the modification strength in order to preserve the fidelity of the image. We devise a genetic algorithm that can explore decision boundaries in an artificially constrained search space to improve the attack effect as well as construct aggressive covert adversarial samples by detecting "sensitive pixels" in ordinary samples to place discontinuous perturbations. The results reveal that the stego-image has good visual quality and attack effect. To the best of our knowledge, this is the first attempt to use covert data hiding to generate adversarial samples based on GAN.
Hyperbolic Knowledge Transfer with Class Hierarchy for Few-Shot Learning

Few-shot learning (FSL) aims to recognize a novel class with very few instances, which is a challenging task since it suffers from a data scarcity issue. One way to effectively alleviate this issue is introducing explicit knowledge summarized from human past experiences to achieve knowledge transfer for FSL. Based on this idea, in this paper, we introduce the explicit knowledge of class hierarchy (i.e., the hierarchy relations between classes) as FSL priors and propose a novel hyperbolic knowledge transfer framework for FSL, namely, HyperKT. Our insight is, in the hyperbolic space, the hierarchy relation between classes can be well preserved by resorting to the exponential growth characters of hyperbolic volume, so that better knowledge transfer can be achieved for FSL. Specifically, we first regard the class hierarchy as a tree-like structure. Then, 1) a hyperbolic representation learning module and a hyperbolic prototype inference module are employed to encode/infer each image and class prototype to the hyperbolic space, respectively; and 2) a novel hierarchical classification and relation reconstruction loss are carefully designed to learn the class hierarchy. Finally, the novel class prediction is performed in a nearest-prototype manner. Extensive experiments on three datasets show our method achieves superior performance over state-of-the-art methods, especially on 1-shot tasks.
Self-Supervised Contrastive Representation Learning in Computer Vision

Although its origins date a few decades back, contrastive learning has recently gained popularity due to its achievements in self-supervised learning, especially in computer vision. Supervised learning usually requires a decent amount of labeled data, which is not easy to obtain for many applications. With self-supervised learning, we can use inexpensive unlabeled data and achieve a training on a pretext task. Such a training helps us to learn powerful representations. In most cases, for a downstream task, self-supervised training is fine-tuned with the available amount of labeled data. In this study, we review common pretext and downstream tasks in computer vision and we present the latest self-supervised contrastive learning techniques, which are implemented as Siamese neural networks. Lastly, we present a case study where self-supervised contrastive learning was applied to learn representations of semantic masks of images. Performance was evaluated on an image retrieval task and results reveal that, in accordance with the findings in the literature, fine-tuning the self-supervised training showed the best performance.
Quantum Natural Language Processing Based Sentiment Analysis Using Lambeq Toolkit

Sentiment classification is one the best use case of classical natural language processing (NLP) where we can witness its power in various daily life domains such as banking, business and marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology which has the potential to provide quantum advantage for NLP tasks. In this paper we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and a decent accuracy for experiments ran on a noisy quantum device. We utilize the lambeq QNLP toolkit and $t|ket>$ by Cambridge Quantum (Quantinuum) to bring out the results.
Quantum readout and gradient deep learning model for secure and sustainable data access in IWSN

The industrial wireless sensor network (IWSN) is a surface-type of wireless sensor network (WSN) that suffers from high levels of security breaches and energy consumption. In modern complex industrial plants, it is essential to maintain the security, energy efficiency, and green sustainability of the network. In an IWSN, sensors are connected to the Internet in a non-monitored environment. Hence, non-authorized sensors can retrieve information from the IWSN. Therefore, to ensure that data access between sensors remains sustainable and secure, energy-efficient authentication and authorization are required. In this article, a novel Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model is proposed to ensure that only trustworthy sensors can access IWSN data. The major objective of this QR-GSDL model is to create secure, energy-efficient IWSN to attain green sustainability and reduce the industrial impact on the environment. First, using the quantum readout and hash function, a registration method is designed to efficiently perform the registration process. Next, a gradient secured deep learning method is adopted to implement the authentication and authorization process in order to ensure energy-saving and secure data access. Simulations are conducted to evaluate the QR-GSDL model and compare its performance with that of three well-known models: online threshold anomaly detection, machine learning-based anomaly detection, and dynamic CNN. The simulation outcomes show that the proposed model is secure and energy-efficient for use in the IWSN. Moreover, the experimental results prove that the QR-SGDL model outperforms the existing models in terms of energy consumption, authentication rate, authentication time, and false acceptance rate.
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions

Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers.Existing methods handle this task by summarizing each role's content separately and thus are prone to ignore the information from other roles.However, we believe that other roles' content could benefit the quality of summaries, such as the omitted information mentioned by other roles.Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization.It adopts cross attention and decoder self-attention interactions to interactively acquire other roles' critical information.The cross attention interaction aims to select other roles' critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles' summaries.Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets.Extensive analyses have demonstrated that other roles' content could help generate summaries with more complete semantics and correct topic structures.
Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone

This paper proposes a method for simultaneously planning a path and a sequence of deformations for a tensegrity drone. Previous work in the field required the use of bounding surfaces, making the planning more conservative. The proposed method takes advantage of the need to use mixed-integer variables in choosing the drone path (using big-M relaxation) to simultaneously choose the configuration of the drone, eliminating the need to use semidefinite matrices to encode configurations, as was done previously. The numerical properties of the algorithm are demonstrated in numerical studies. To show the viability of tensegrity drones, the first tensegrity quadrotor Tensodrone was build. The Tensodrone is based on a six-bar tensegrity structure that is inherently compliant and can withstand crash landings and frontal collisions with obstacles. This makes the robot safe for the humans around it and protects the drone itself during aggressive maneuvers in constrained and cluttered environments, a feature that is becoming increasingly important for challenging applications that include cave exploration and indoor disaster response.