Reviewerly/exHarmony huggingface dataset:
2nd Workshop on Augmented Intelligence in Technology-Assisted Review Systems (ALTARS)

In this second edition of the workshop on Augmented Intelligence in Technology-Assisted Review Systems (ALTARS), we focus on the evaluation of High-recall Information Retrieval (IR) systems which tackle challenging tasks that require the finding of (nearly) all the relevant documents in a collection. In fact, despite the number of evaluation measures at our disposal to assess the effectiveness of a “traditional” retrieval approach, there are additional dimensions of evaluation for these systems. During the workshop, the organizers as well as the participants will discuss these issues and prepare a set of guidelines for the preparation of a correct evaluation of these kinds of systems.
An Overview of Different Types of Recommendations Systems - A Survey

In recent years the boom of internet and social media usage everyone spend their invaluable time in social media app and looking for the solution for all kind of their problems. This work analysis deeply on how recommendation system works and its types in different platforms. Most of the modern recommendation system use machine learning algorithms like linear regression, random forest regression and support vector model with collaborative filtering method. Recommendation is nothing but an choice making system. It is vary from person to person based on their interest, culture, locality, education background, interpersonal skills etc., The huge item can be filtered from one by one based on each parameter and finally it will reach the right recommendation item. The research community has worked tremendous way in the field of recommendation system and produced huge variety of result. This survey enlightening the ideas about variety of recommendation system and techniques used by the research community.
Segmentation of breast molybdenum target image lesions based on semi-supervised fuzzy clustering

Currently, breast cancer is one of the most common cancers among women. To aid clinicians in diagnosis, lesion regions in mammography pictures can be segmented using an artificial intelligence system. This has significant clinical implications. Clustering algorithms, as unsupervised models, are widely used in medical image segmentation. However, due to the different sizes and shapes of lesions in mammography images and the low contrast between lesion areas and the surrounding pixels, it is difficult to use traditional unsupervised clustering methods for image segmentation. In this study, we try to apply the semisupervised fuzzy clustering algorithm to lesion segmentation in mammography molybdenum target images and propose semisupervised fuzzy clustering based on the cluster centres of labelled samples (called SFCM_V, where V stands for cluster centre). The algorithm refers to the cluster centre of the labelled sample dataset during the clustering process and uses the information of the labelled samples to guide the unlabelled samples during clustering to improve the clustering performance. We compare the SFCM_V algorithm with the current popular semisupervised clustering algorithm and an unsupervised clustering algorithm and perform experiments on real patient mammogram images using DICE and IoU as evaluation metrics; SFCM_V has the highest evaluation metric coefficient. Experiments demonstrate that SFCM_V has higher segmentation accuracy not only for larger lesion regions, such as tumours, but also for smaller lesion regions, such as calcified spots, compared with existing clustering algorithms.
Self-Supervised Learning of Depth and Ego-Motion for 3D Perception in Human Computer Interaction

3D perception of depth and ego-motion is of vital importance in intelligent agent and Human Computer Interaction (HCI) tasks, such as robotics and autonomous driving. There are different kinds of sensors that can directly obtain 3D depth information. However, the commonly used Lidar sensor is expensive, and the effective range of RGB-D cameras is limited. In the field of computer vision, researchers have done a lot of work on 3D perception. While traditional geometric algorithms require a lot of manual features for depth estimation, Deep Learning methods have achieved great success in this field. In this work, we proposed a novel self-supervised method based on Vision Transformer (ViT) with Convolutional Neural Network (CNN) architecture, which is referred to as ViT-Depth . The image reconstruction losses computed by the estimated depth and motion between adjacent frames are treated as supervision signal to establish a self-supervised learning pipeline. This is an effective solution for tasks that need accurate and low-cost 3D perception, such as autonomous driving, robotic navigation, 3D reconstruction, and so on. Our method could leverage both the ability of CNN and Transformer to extract deep features and capture global contextual information. In addition, we propose a cross-frame loss that could constrain photometric error and scale consistency among multi-frames, which lead the training process to be more stable and improve the performance. Extensive experimental results on autonomous driving dataset demonstrate the proposed approach is competitive with the state-of-the-art depth and motion estimation methods.
Learning defense transformations for counterattacking adversarial examples

Deep neural networks (DNNs) are vulnerable to adversarial examples with small perturbations. Adversarial defense thus has been an important means which improves the robustness of DNNs by defending against adversarial examples. Existing defense methods focus on some specific types of adversarial examples and may fail to defend well in real-world applications. In practice, we may face many types of attacks where the exact type of adversarial examples in real-world applications can be even unknown. In this paper, motivated by that adversarial examples are more likely to appear near the classification boundary and are vulnerable to some transformations, we study adversarial examples from a new perspective that whether we can defend against adversarial examples by pulling them back to the original clean distribution. We empirically verify the existence of defense affine transformations that restore adversarial examples. Relying on this, we learn defense transformations to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs. Extensive experiments on both toy and real-world data sets demonstrate the effectiveness and generalization of our defense method. The code is avaliable at https://github.com/SCUTjinchengli/DefenseTransformer.
Testing SOAR tools in use

Investigations within Security Operation Centers (SOCs) are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, correlate the data into information, and then document results in a ticketing system. Security Orchestration, Automation, and Response (SOAR) tools are a relatively new technology that promise, with appropriate configuration, to collect, filter, and display needed diverse information; automate many of the common tasks that unnecessarily require SOC analysts' time; facilitate SOC collaboration; and, in doing so, improve both efficiency and consistency of SOCs. There has been no prior research to test SOAR tools in practice; hence, understanding and evaluation of their effect is nascent and needed. In this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and six commercial SOAR tools. Our contributions include the experimental design, itemizing six defining characteristics of SOAR tools, and a methodology for testing them. We describe configuration of a cyber range test environment, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. Concisely, our findings are that: per-SOC SOAR configuration is extremely important; SOAR tools increase efficiency and reduce context switching, although with potentially decreased ticketing accuracy/completeness; user preference is slightly negatively correlated with their performance with the tool; internet dependence varies widely among SOAR tools; and balance of automation with assisting decision making is preferred by senior participants. We deliver a public user- and tool-anonymized and -obfuscated version of the data.
Geometric-Feature Representation Based Pre-Training Method for Reinforcement Learning of Peg-in-Hole Tasks

Recently, reinforcement learning (RL) is often used for learning the strategy of peg-in-hole tasks. However, traditional state representation of PiH RL might be either redundant or abstract, which leads to unnecessary learning steps and incompatibility with mathematical training optimization. To issue these problems, a geometric-feature (GF) state representation method for peg-in-hole DDPG (Deep Deterministic Policy Gradient) RL is proposed to get rid of the redundant states. Also, a network pre-training method for peg-in-hole DDPG based on GF representation is provided to help DDPG acquire basic assembly skills before training. Finally, we executed the simulation experiments in the environment with Open AI Gym + Pybullet to test the performance of the proposed GF representation based pre-training method. We also executed the lenrned strategy on the real-world Panda robot.
A Novel QIA Protocol Based on Bell States Position by Random Selection

Identifying trusted user plays an important role before initiating secure communication; therefore, a quantum authentication schemes is proposed based on Bell pair. Two trusted party initial shared a common secret key (known as pre-shared key) which only known to them. Various existing protocol uses quantum resources with memory for Authentication process but the storage time of qubits in 3 ns. In proposed protocol, authentication process had done without strong the Bell states by trusted parties. Sending party selects 4 classical bits to form Bell pair in any one of the positions {(1, 2)(3, 4)} or {(1, 3)(2, 4)} based on the consecutive bits in pre-shared key. The receiving party uses the consecutive bits in pre-shared key to decode the information about authentication key using Bell state measurement. Adversary has no knowledge about pre-shared key and guessing incorrect position makes entangle swapping of Bell states. Adversary incorrect position leads to its identification by trusted parties by verifying the authentication key in classical channel. The security of proposed protocol is analyzed under intercept measure and resend attack (IR). In addition, proposed protocol can prevent the adversary to fetch the information about the pre-shared key. Various existing protocol is compared with the proposed one with quantum resources and memory requirement. The proposed protocol is implemented in IBM Quantum Lab, and circuit simulation is shown visually in IBM quantum composer.
Tapered whisker reservoir computing for real-time terrain identification-based navigation

This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain.
Exploring the Potential of Metaverse as a Future Learning Platform for Enhancing EFL Learners’ English Proficiency

The aim of this study was to focus on describing how different learning platforms (traditional and Metaverse) may influence learners’ English proficiency. To investigate whether the experience of a Metaverse environment can be favorable for speaking practice, forty-five students were divided into two groups: traditional and Metaverse. During the semester, students in the traditional group practiced speaking English in offline classrooms, while those in the Metaverse group exercised speaking English in a Metaverse environment. According to the findings, both groups significantly improved their English proficiency within the group, but there were no significant differences between the two groups. In terms of students’ perspectives, the advantages and disadvantages of the Metaverse learning platform shed light on how they perceived using it for learning English speaking. The results of this study also suggest pedagogical implications and insights for future research.
Energy Efficient Path Planning for 3D Aerial Inspections

The use of Unmanned Aerial Vehicles (UAVs) in different inspection tasks is increasing. This technology reduces inspection costs and collects high quality data of distinct structures, including areas that are not easily accessible by human operators. However, the reduced energy available on the UAVs limits their flight endurance. To increase the autonomy of a single flight, it is important to optimize the path to be performed by the UAV, in terms of energy loss. Therefore, this work presents a novel formulation of the Travelling Salesman Problem (TSP) and a path planning algorithm that uses a UAV energy model to solve this optimization problem. The novel TSP formulation is defined as Asymmetric Travelling Salesman Problem with Precedence Loss (ATSP-PL), where the cost of moving the UAV depends on the previous position. The energy model relates each UAV movement with its energy consumption, while the path planning algorithm is focused on minimizing the energy loss of the UAV, ensuring that the structure is fully covered. The developed algorithm was tested in both simulated and real scenarios. The simulated experiments were performed with realistic models of wind turbines and a UAV, whereas the real experiments were performed with a real UAV and an illumination tower. The inspection paths generated presented improvements over 24% and 8%, when compared with other methods, for the simulated and real experiments, respectively, optimizing the energy consumption of the UAV.
Matching Images from Different Viewpoints with Deep Learning Based on LoFTR and MAGSAC++

Matching 2D images from different viewpoints plays a crucial role in the fields of Structure-from-Motion and 3D reconstruction. However, image matching for assorted and unstructured images with a wide variety of viewpoints leads to difficulty for traditional matching methods. In this paper, we propose a Transformer-based feature matching approach to capture the same physical points of a scene from two images with different viewpoints. The local features of images are extracted by the LoFTR, which is a detector-free deep-learning matching model on the basis of Transformer. The subsequent matching process is realized by the MAGSAC++ estimator, where the matching results are summarized in the fundamental matrix as the model output. By removing image feature points with low confidence scores and applying the test time augmentation, our approach can reach a mean Average Accuracy 0.81340 in the Kaggle competition Image Matching Challenge 2022. This score ranks 45/642 in the competition leaderboard, and can get a silver medal in this competition. Our work could help accelerate the research of generalized methods for Structure-from-Motion and 3D reconstruction, and would potentially deepen the understanding of image feature matching and related fields.
Evaluation of Explanation Methods of AI - CNNs in Image Classification Tasks with Reference-based and No-reference Metrics

The most popular methods in AI-machine learning paradigm are mainly black boxes. This is why explanation of AI decisions is of emergency. Although dedicated explanation tools have been massively developed, the evaluation of their quality remains an open research question. In this paper, we generalize the methodologies of evaluation of post-hoc explainers of CNNs’ decisions in visual classification tasks with reference and no-reference based metrics. We apply them on our previously developed explainers (FEM1 , MLFEM), and popular Grad-CAM. The reference-based metrics are Pearson correlation coefficient and Similarity computed between the explanation map and its ground truth represented by a Gaze Fixation Density Map obtained with a psycho-visual experiment. As a no-reference metric, we use stability metric, proposed by Alvarez-Melis and Jaakkola. We study its behaviour, consensus with reference-based metrics and show that in case of several kinds of degradation on input images, this metric is in agreement with reference-based ones. Therefore, it can be used for evaluation of the quality of explainers when the ground truth is not available.
A case study on providing FAIR and metrologically traceable data sets

In recent years, data science and engineering have faced many challenges concerning the increasing amount of data. In order to ensure findability, accessibility, interoperability, and reusability (FAIRness) of digital resources, digital objects as a synthesis of data and metadata with persistent and unique identifiers should be used. In this context, the FAIR data principles formulate requirements that research data and, ideally, also industrial data should fulfill to make full use of them, particularly when Machine Learning or other data-driven methods are under consideration. In this contribution, the process of providing scientific data of an industrial testbed in a traceable and FAIR manner is documented as an example.
Huffman Encoding with White Tailed Eagle Algorithm-based Image Steganography Technique

Since the Internet is a medium for transporting sensitive data, the privacy of the message transported has become a major concern. Image steganography has become a prominent tool for hiding data to ensure privacy during transfer. An efficient steganography system is essential to accomplish the best embedding capacity and maintain the other parameters at a satisfying level. Image encryption systems provide a secure and flexible system to maintain the privacy of image conversion and storage in the transmission system. Many existing image steganography methods can be attacked by various techniques, or do not support many image formats for embedding. To resolve these shortcomings, this study presents the Huffman Encoding with White Tailed Eagle Algorithm-based Image Steganography (HEWTEA-IS) technique, aiming to achieve secrecy with no compromise in image quality. The HEWTEA-IS method uses Discrete Wavelet Transform (DWT) for the decomposition of images into different subbands, and Huffman encoding to determine the embedding bits on the decomposed blocks and offer an additional layer of security. Moreover, the WTEA resolves the problem of imperceptibility by identifying the optimal probable position in the cover image for embedding secret bits. The proposed algorithm was simulated and examined in terms of different measures, and an extensive experimental analysis ensured that it is superior to other methods in several aspects.
Customer Experience in the E-Commerce Market Through the Virtual World of Metaverse

The present study critically analyses the online shopping experience of consumers and suggests the firm change the operations to adopt the virtual retail world of metaverse and the contribution of e-commerce towards economic growth. All the sectors, including transportation, education, and infrastructure, as well as employment and government, have potential impact on e-commerce. The obstacles to electronic commerce that influence sellers and shoppers will be diminished due to the transformation to the virtual world of metaverse. As UAE is the place for successful implementation of business innovations, a case study of Souq.com acquired by Amazon has been discussed in this study. This case study focuses on fruitful contribution of e-commerce market towards UAE's economic growth and the elements that drive e-commerce development. The study concludes with the identification of e-commerce factors, which has an impact on online shopping experience of metaverse consumers, and addressing the issues that stimulate economic growth through virtual world of metaverse.
Financial, Accounting and Tax Implications of Ransomware Attack

Ransomware is a prime cybersecurity threat at the moment. In this paper we analyze financial implications of ransomware attacks, motivation of the ransomware victim to pay ransom, and legal, accounting and tax implications of such payment. The methodological approach used in the study is a combination of formal-dogmatic method and argumentative literature review. First, we provide an overview of all potential losses which could be incurred by the ransomware attack. Further, we analyze under which conditions is legal to pay any kind of ransom, including cyber ransom, as an organization as well as which other considerations victims should consider when deciding to pay ransom. In that respect we analyze accounting and tax implications of losses inflicted by the ransomware attack, putting special attention to the ransom payments.
Review on the Gamma Function and Error Correction

This paper presents a newly extended gamma function for positive real number x≥1 and correct the error in the existing gamma function. The newly extended gamma function is a special function to compute the factorial for positive real values by the integral.
Digital transformation of education in the context of the realities of the information society: problems, prospects

Scientific exploration aims to identify the level of synergy in the educational environment between society and the digital world. The task is to determine the balance between traditional social principles and innovative information and digital technologies. The methodology of scientific research is provided by a general scientific and philosophical-scientific arsenal, focused on social-humanitarian and information-technological dimensions. The level of influence of the information and digital cluster on the public understanding of education remains a debatable issue. There are increasing reservations about the failure of society to respond appropriately to the risks associated with the digitalization of education. The findings of this research point to two dichotomously oriented formats of manifestation of the digitalization of education in the socio-cultural space: internal-external and horizontal-vertical. A promising direction of the study of digital transformations in the context of the information society is to develop balances through which digitalization will introduce an innovative model in the educational system without violating the traditional educational paradigms, concerning primarily the multinomial aspect in education. The results of the study highlight the controlling and regulating function of the information dimension as a prerequisite for the effectiveness of innovation processes in education.