Reviewerly/exHarmony huggingface dataset:
Adversarial Machine Learning: The Rise in AI-Enabled Crime and its Role in Spam Filter Evasion

The rise in frequency and consequence of AI-enabled cyber-crimes has been a cause of concern for decades. The development of defensive maneuvers to combat the same has been gaining traction. However, this paper attempts to look at the flip side of the issue, and go into the mechanics of offence. Cases of voice mimicking used for crime, and natural processing algorithms absorbing harmful and offensive human text patterns to create problematic virtual situations based on these inputs have been discussed using examples of voice cloning, GPT-3 technology and Microsoft’s AI bot, Tay. A study of shadow models has been included. Evasion, infiltration and manipulation of machine learning models through shadow modelling techniques are on the rise due to their straightforward development methods, allowing the identification of shortcomings in input features, which can cause misclassification by the model. With a special focus on spam filters, their structure, and evasion techniques, we look at the ways in which artificial intelligence is being utilised to cause harm, concluding with a final analysis of the Proofpoint evasion case. From an academic standpoint, we hope this research offers some insight on how to combat the problem by knowing the details of the opponent. Potential future advancements in the field of AI-enabled crime are also explored.
Decentralized CRL Management for Vehicular Networks With Permissioned Blockchain

In vehicular networks, authentication technology provides a basic security means to achieve trusted communication between legitimate vehicles. Revocation checking for vehicle certificates is an indispensable procedure in the process of authentication to protect vehicular networks from attacks by non-legitimate vehicles. However, revocation checking introduces procedures that requires additional time to process which challenges latency-sensitive applications in vehicular networks. This challenge grows more evidently when considering the factor of privacy preservation. In this paper, we propose to offload partial revocation task to network edges to lighten the revocation process in vehicles. Particularly, we design a method to achieve decentralized Global Certificate Revocation List (GCRL) management in network edges using permissioned blockchain technologies. In the method, both tamper-proof property and quick synchronization of the GCRL are guaranteed. Besides, our method can detect illegal revocation behaviours by validation in vehicles. Finally, we build a prototype of our proposed solution based on Hyperledger Fabric, and compare with Proof-of-Work scheme in terms of blockchain synchronization latency performance.
Prednosti i nedostaci nastave na daljinu iz ugla nastavnika i roditelja

The corona virus pandemic has brought online teaching and distance learning to the forefront, which is very different from typical classroom teaching. The aim of this paper was to determine the differences in the perception of the advantages and disadvantages of online teaching between primary school teachers and parents of primary school children. The sample consisted of a total of 1120 respondents, out of which 570 are teachers employed in primary schools (50.9%) and 550 (49.1%) are parents of children attending primary schools in Serbia. A questionnaire was created for the purposes of this research and it was applied in an online form. The results of this research show that the perception of the perceived advantages and disadvantages of online teaching and distance learning from the teachers' and parents' point of view differs significantly, and that teachers have a more intense experience, both in terms of advantages and disadvantages of this type of teaching process. According to both parents and teachers, the most significant disadvantage of online teaching is the lack of direct contact with pupils. From the obtained results it is possible to conclude that a step forward is needed in terms of further development of teacher competencies and resources for new professional challenges, as well as in terms of guiding and empowering parents for a more active role in the process of online education of their children.
FCSM: Fuzzy Cloud Selection Model using QoS Parameters

The global demand of web users for Cloud computing in the various public domain has gained tremendous attention for the Cloud service providers (CSP) to satisfy the specifications of different Cloud users. A number of challenges such as migration of data from one Cloud to another, dynamic demand handing of Cloud resources, allocation of suitable Cloud to the Cloud users are the key challenges of Cloud computing that can reduce the computing capabilities offered by the Cloud. The adoption of the Cloud has spurred competition among CSPs because each CSP provides cloud services at varying costs and with varying options. To improve the Quality of Service (QoS) experience of Cloud users and overcome the challenge of choosing a suitable Cloud to meet the specific requirements of users in different applications, a Fuzzy Cloud Selection Model (FCSM) is proposed with different selection parameters.
Dubins Path-Oriented Rapidly Exploring Random Tree* for Three-Dimensional Path Planning of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) do not collide with obstacles, generate a path in real-time, and must fly to the target point. The sampling-based rapidly exploring random tree (RRT) algorithm has the advantages of fast computation and low computational complexity. It is suitable for real-time path generation, but the optimal path cannot be guaranteed. Further, the direction of the flight and the minimum radius of rotation have not been taken into account for the characteristics of the UAVs. This work proposes a Dubins path-oriented RRT* algorithm, which applies the Dubins path to the RRT algorithm to consider the direction of flight and the minimum radius of rotation and improves optimality and convergence. The proposed algorithm sets the sample node as the target point, orients toward the Dubins path, and then generates a tree. To verify the performance of the proposed algorithm, it is compared with existing RRT algorithms. As a result of performance analysis, the proposed algorithm improved the path length by 14.87% and the calculation time by 82.36%. Finally, the algorithm’s performance is verified by applying the proposed algorithm to a fixed-wing UAV and performing a numerical analysis of the generated path.
Real-Time Image Based Weapon Detection Using YOLO Algorithms

From last few years country faces major challenge in maintaining security standards particularly in public and highly sensitive places such as airports, movie theatres, stadiums, and national parks, etc. The offsite and onsite planers use many tactics to resist authority and disrupt and to add turmoil in order to achieve their goals and objectives. These tactics can be planned or unplanned. Many crowd management experts suggest that the availability of suspicious objects such as Camera, Handgun, Rifles, Dagger, Sword and Sticks at sight before or during the event can be an indication of upcoming threat or any unlawful activities and their identification may help security forces in their proactive management and control of any destructive activities. In this research work, we generated a novel dataset “DIAT-Weapon” Dataset for weapon object detection using web scraping techniques. DIAT-Weapon Dataset consists of 2712 images divided into six categories mainly: Camera, Handgun, Rifles, Dagger, Sword and Sticks. We customized and fine-tuned YOLOv4 models to classify and position the six types of harmful objects i.e. Camera, Handgun, Rifles, Dagger, Sword, and Sticks in real time. To achieve real-time faster performance and better detection accuracy, YOLOv4 is fine-tuned, and the preset anchors trained on DIAT-Weapon annotated dataset. Using a series of YOLOv4 object detection algorithms, we demonstrated experiments on our dataset, achieving 0.63 mAP. To our best knowledge, this is the first work that utilizes customized YOLOv4 model for real-time localization and classification of weapon objects into six different categories. To our best knowledge, this is the first work that utilizes customized YOLOv4 model for real time localization and classification of weapon objects into six different categories.
Pheno-mapper

High-throughput technologies to collect field data have made observations possible at scale in several branches of life sciences. The data collected can range from the molecular level (genotypes) to physiological (phenotypic traits) and environmental observations (e.g., weather, soil conditions). These vast swathes of data, collectively referred to as phenomics data, represent a treasure trove of key scientific knowledge on the dynamics of the underlying biological system. However, extracting information and insights from these complex datasets remains a significant challenge owing to their multidimensionality and lack of prior knowledge about their complex structure. In this paper, we present Pheno-Mapper, an interactive toolbox for the exploratory analysis and visualization of large-scale phenomics data. Our approach uses the mapper framework to perform a topological analysis of the data, and subsequently render visual representations with built-in data analysis and machine learning capabilities. We demonstrate the utility of this new tool on real-world plant (e.g., maize) phenomics datasets. In comparison to existing approaches, the main advantage of Pheno-Mapper is that it provides rich, interactive capabilities in the exploratory analysis of phenomics data, and it integrates visual analytics with data analysis and machine learning in an easily extensible way. In particular, Pheno-Mapper allows the interactive selection of subpopulations guided by a topological summary of the data and applies data mining and machine learning to these selected subpopulations for in-depth exploration.
A Deep Learning Based Method for Network Application Classification in Software-Defined IoT

Network Application Classification (NAC) is a vital technology for intrusion detection, Quality-of-Service (QoS)-aware traffic engineering, traffic analysis, and network anomalies. Researchers have focused on designing algorithms using deep learning models based on statistical information to address the challenges of traditional payload and port-based traffic classification techniques. Internet of Things (IoT) and Software Defined Network (SDN) are two popular technologies nowadays and aims to connect devices over the internet and intelligently control networks from a centralized space. IoT aims to connect billions of devices; therefore, classification is essential for efficient processing. SDN is a new networking paradigm, which separates data plane measurement from the control plane. The emergence of deep learning algorithms with SDN provides a scalable traffic classification architecture. Due to the inadequate results of payload and port-based approaches, a statistical technique to classify network traffic into different classes using a Convolution Neural Network (CNN) and a Recurrent Neural Network (RNN) is presented in this paper. This paper provides a classification method for software defined IoT networks. The results show that, contrary to other traffic classification methods, the proposed approach offered a better accuracy rate of over 99 %, which is promising.
A Meta Analysis on Augmented Reality Application for Individuals with Intellectual Disability

There is a growing interest in utilizing technology tools such as augmented reality to support individuals with disability in general and intellectual disabilities in particular. Therefore, the purpose of this study is to assess the effectiveness of augmented reality technology in improving positive outcomes of individual with intellectual disabilities. Following Arksey and O’Malley and Kitchenham guideline, a meta-analysis of the overall effect of augmented reality on individuals with intellectual disabilities was conducted. Twenty-two studies were considered to explore the effect of augmented reality technology on the outcomes of individuals with intellectual disabilities. The result of the majority of the studies indicated that augmented reality technology was effective in improving positive outcomes among individuals with intellectual disabilities. Hence, the study offers the benefits of utilizing augmented reality technology in special needs setting and could be a successful technology among individuals with intellectual disabilities.
A Study of Human–AI Symbiosis for Creative Work: Recent Developments and Future Directions in Deep Learning

Recent advances in Artificial Intelligence (AI), particularly deep learning, are having an enormous impact on our society today. Record numbers of jobs previously held by people have been automated, from manufacturing to transportation to customer services. The concerns of AI replacing humans by taking over people's jobs need to be urgently addressed. This article investigates some promising different directions of AI development: Instead of using AI to replace people, we should use AI to team up with people so that both can work better and smarter. Human–AI symbiosis refers to people and AI working together to jointly solve problems and perform specific tasks. The recent developments in deep learning models and frameworks have significantly improved the efficiency and performance of human and AI collaborations. In this article, some research work on human–AI collaborative environments has been extensively studied and analyzed to reveal the progress in this field. Although the teaming of humans and machines includes many complex tasks, the development has been very promising. One of the main goals in this field is to develop additional capabilities in machines capable of being successful teammates with a human partner. The correctness of the outcomes is often determined by the underlying technology and how performance and human satisfaction are measured through the collaborative nature of the system. We conclude that the teaming of humans and AI, particularly deep learning, has the advantage of combining the power of AI with the human domain expertise to improve performance and create value. Human–AI symbiosis could be a promising future direction for AI's continuing integration into the world.
A comprehensive and systematic literature review on the employee attendance management systems based on cloud computing

Abstract Attendance is critical to the success of any business or industry. As a result, most businesses and institutions require a system to track staff attendance. On the other hand, cloud computing technology is being utilized in the human resource management sector. It may be an excellent option for processing and storing large amounts of data and improving management effectiveness to a desirable level. Hence, this paper examines cloud infrastructures for employee attendance management in which the articles are categorized into three groups. The results show that cloud infrastructure has a significant and positive impact on the management of employee attendance systems. Also, the results reveal that the radio frequency identification authentication protocol protects the privacy of tags and readers against database memory. When references operate properly, they help the people concerned and society by making workplaces more efficient and safer.
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning

Multiple-choice VQA has drawn increasing attention from researchers and end-users recently. As the demand for automatically constructing large-scale multiple-choice VQA data grows, we introduce a novel task called textual Distractors Generation for VQA (DG-VQA) focusing on generating challenging yet meaningful distractors given the context image, question, and correct answer. The DG-VQA task aims at generating distractors without ground-truth training samples since such resources are rarely available. To tackle the DG-VQA unsupervisedly, we propose GOBBET, a reinforcement learning(RL) based framework that utilizes pre-trained VQA models as an alternative knowledge base to guide the distractor generation process. In GOBBET, a pre-trained VQA model serves as the environment in RL setting to provide feedback for the input multi-modal query, while a neural distractor generator serves as the agent to take actions accordingly. We propose to use existing VQA models’ performance degradation as indicators of the quality of generated distractors. On the other hand, we show the utility of generated distractors through data augmentation experiments, since robustness is more and more important when AI models apply to unpredictable open-domain scenarios or security-sensitive applications. We further conduct a manual case study on the factors why distractors generated by GOBBET can fool existing models.
Real-Time Classification of Anthropogenic Seismic Sources from Distributed Acoustic Sensing Data: Application for Pipeline Monitoring

Abstract Distributed Acoustic Sensing (DAS) is an innovative method to record acoustic waves using an optical fiber as a network of sensors. Current DAS devices can monitor up to 50 km of optical fiber and the use of optical repeaters can raise even more this length, while allowing a spatial discretization of the order of a meter. Handling such amount of data is a challenge in terms of data management and data analysis (such as event source identification), more specifically for monitoring applications such as infrastructures or natural hazards. In this work, we propose a processing chain for real-time classification of anthropogenic sources using a combination of Random Forest (RF) and Random Markov Field (RMF). To develop the method, we choose to focus on the application of pipeline monitoring. The algorithm is therefore trained to recognize six classes of seismic sources: pedestrian, impact, backhoe, compactor, leak, and noise. All the sources were triggered and recorded on our own test bench under controlled conditions. The average sensitivity of our processing chain reaches 83% with the use of only RF and achieves 87% in combination with RMF. Classification maps show that the RMF approach can increase the average sensitivity by removing isolated signals. In addition to this improvement in sensitivity, this new approach also permits to identify synchronous events taking place at nearby positions, which is difficult with classical methods.
Be My Guess: Guessing Entropy vs. Success Rate for Evaluating Side-Channel Attacks of Secure Chips

In a theoretical context of side-channel attacks, optimal bounds between success rate and guessing entropy are derived with a simple majorization (Schur-concavity) argument. They are further theoretically refined for different versions of the classical Hamming weight leakage model, in particular assuming a priori equiprobable secret keys and additive white Gaussian measurement noise. Closed-form expressions and numerical computation are given. A study of the impact of the choice of the substitution box with respect to side-channel resistance reveals that its nonlinearity tends to homogenize the expressivity of success rate and guessing entropy. The intriguing approximate relation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GE=1/SR$</tex> is observed in the case of 8-bit bytes and low noise.
Decentralized Multi-target Tracking in Urban Environments: Overview and Challenges

In multi-target tracking, sensor control involves dynamically configuring sensors to achieve improved tracking performance. Many of these techniques focus on sensors with memoryless states (e.g., waveform adaptation, beam scheduling, and sensor selection), lending themselves to computationally efficient control strategies. Mobile sensor control for multi-target tracking, however, is significantly more challenging due to the complexity of the platform state dynamics. This platform complexity necessitates high-fidelity, non-myopic control strategies in order to achieve strong tracking performance while maintaining safe operation. These sensor control techniques are particularly important in non-cooperative urban surveillance applications including person of interest, vehicle, and unauthorized UAV interdiction. In this overview paper, we highlight the current state of the art in mobile sensor control for multi-target tracking in urban environments. We use this application to motivate the need for closer collaboration between the information fusion, tracking, and control research communities across three challenge areas relevant to the urban surveillance problem.
Improving precision of objective image/video quality meters

Abstract Although subjective test is the most accurate image/video quality assessment tool, it is extremely time demanding. In the past two decades, a variety of objective quality measuring tools, such as SSIM, IW-SSIM, SPSIM, FSIM, etc., have been devised, that well correlate with the subjective tests results. However, the main problem with these methods is that they do not discriminate the measured quality well enough, especially at high quality range. In this article we show how the accuracy/precision of these Image Quality Assessment (IQA) meters can be increased by mapping them into a Logistic Function (LF). The precisions are tested over a variety of image/video databases. Our experimental tests indicate while the used high-quality images can be discriminated by 23% resolution on the MOS subjective scores, discrimination resolution by the widely used IQAs are only 2%, but their mapped IQAs to Logistic Function at this quality range can be improved to 9 − 17%, depending on the characteristics of the LF function. Moreover, their precision at low to mid quality range can also be improved. At this quality range, while the discrimination resolution of MOS of the tested images is 23.2%, those of raw IQAs is nearly 8.9%, but discrimination of their adapted logistic functions can be very close to that of MOS. Moreover, with the used image databases the Pearson Linear Correlation Coefficient (PLCC) of MOS with the logistic function can be improved by 2 − 20% as well.
Reversible Multi-Level Watermarking Scheme for Color Images

Reversible data hiding is to embed data into digital multimedia, while the original multimedia and embedded data can be restored from the watermarked one without any loss. In this paper, we propose a novel Reversible Multi-Level Watermarking (RMLW) scheme for color images with grayscale invariance. Being different from the traditional reversible data hiding methods, we adapt the gray scale-invariant reversible watermarking method which keeps the grayscale of image unchanged as the information is embedded. In the RMLW, one feature region of high robustness is extracted and into which the watermarks are then embedded for multiple times. Lots of experiments have been conducted and the results show that the proposed scheme can extend the capacity efficiently while keep the characteristic of grayscale invariance.
Tokenization-based data augmentation for text classification

Tokenization is one of the most important data preprocessing steps in the text classification task and also one of the main contributing factors in the model performance. However, getting good tokenizations is non-trivial when the input is noisy, and is especially problematic for languages without an explicit word delimiter such as Thai. Therefore, we propose an alternative data augmentation method to improve the robustness of poor tokenization by using multiple tokenizations. We evaluate the performance of our algorithms on different Thai text classification datasets. The results suggest our augmentation scheme makes the model more robust to tokenization errors and can be combined well with other data augmentation schemes.