Reviewerly/exHarmony huggingface dataset:
Detection of DGA-based Malware Communications from DoH Traffic Using Machine Learning Analysis

Encrypted domain name resolution can reduce the risk of privacy leakage for Internet users, but it may also prevent network administrators from detecting suspicious communications. Since operating systems supporting DNS over HTTPS (DoH) have increased in recent years, malware that uses Domain Generation Algorithm (DGA) can exploit it to hide the generated domain names. In this paper, we propose a system that detects DGA-based malware communications from DoH traffic. Based on the concept of hierarchical machine learning analysis, the proposed system classifies network traffic with Gradient Boosting Decision Tree (GBDT) and tree-ensemble models. The evaluation confirmed that the system was able to detect DoH traffic generated by PadCrypt, Sisron, Tinba, and Zloader with 99.12% accuracy. The results indicate that the system has the ability to detect different DGA-based malware communications from DoH traffic with sufficient accuracy to support network administrators.
DroneNet: Crowd Density Estimation using Self-ONNs for Drones

Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios. An interesting application of drone-based video surveillance is to estimate crowd density (both pedestrians and vehicles) in public places. Deep learning using convolution neural networks (CNNs) is employed for automatic crowd counting and density estimation using images and videos. However, the performance and accuracy of such models typically depends upon the model architecture i.e., deeper CNN models improve accuracy at the cost of increased inference time. In this paper, we propose a novel crowd density estimation model for drones (DroneNet) using Self-organized Operational Neural Networks (Self-ONN). Self-ONN provides efficient learning capabilities with lower computational complexity as compared to CNN-based models. We tested our algorithm on two drone-view public datasets. Our evaluation shows that the proposed DroneNet shows superior performance on an equivalent CNN-based model.
Demonstrating Quantum Advantage in Hybrid Quantum Neural Networks for Model Capacity

Quantum machine learning (QML) is an emerging research area that combines quantum computation with classical machine learning (ML). The primary objective of QML is to enhance the performance of traditional ML algorithms by harnessing quantum phenomena. Inspired by the success of classical neural networks (NNs), their quantum analog, commonly known as quantum neural networks (QNNs), are widely being investigated. Despite the significant interest, the literature still lacks some concrete evidence about QNN's superiority over their classical counterparts, especially in practical applications. This paper empirically demonstrates a greater capacity in hybrid quantum neural networks (HQNNs) for a practical application, namely multi-class classification. In particular, we train both the HQNNs and their equivalent classical counterparts on the same data. We then benchmark the models' accuracy for quantifying model's capacity, where greater accuracy typically implies greater capacity. The results demonstrate a clear quantum advantage, i.e., greater capacity of HQNNs over their classical counterparts, where the HQNN models constantly achieve better accuracy. This superiority in performance by HQNNs serves as a foundational study for further investigation to magnify the quantum advantage in real-world applications of HQNNs.
Improvement of Human Pose Estimation and Processing With the Intensive Feature Consistency Network

The modeling of human body kye-points is the most significant aspect of pose estimation appropriately. Computer vision algorithm identifies human pose, body-movement, and action in many ways. Most of the previous works taken advantage for finding accuracy or efficiency in terms of speed. However, many techniques suffer for intensive computational demands with low-latency or higher proceeding speed. We have designed a unique approach for single-person pose estimation and action recognition which is well suited for fitness application and mobility activities. The proposed framework has been developed with a base network that provides an initial pose to further refinement through Intensive Feature Consistency (IFC) network. The IFC network enforces high-level constraints on the global body intensity correction and local body part adjustments. The proposed module reduces the impact of body joint movement diversity by interpreting long-term consistent view. We have illustrated the effectiveness of proposed framework through pose estimation accuracy improvement with two benchmark datasets. Which is specified state-of the- art performance of IFC network under the required real-time processing speed on the CPU platform. The IFC network has improved 99.1% of PCK body and 94.7% of PCK torso accuracy under 31 FPS, which is comparatively higher than the existing work.
A novel Sine–Tangent–Sine chaotic map and dynamic S-box-based video encryption scheme

In this work, we propose a novel one-dimensional (1D) Sine–Tangent–Sine (STS) chaotic map. The proposed STS map exhibits a wider, continuous chaotic range, complex behaviour, and random distribution over the entire data range, when tested for bifurcation diagram, Lyapunov exponent and sample entropy parameters. The work also presents a novel video encryption scheme using the STS map. The proposed scheme mainly comprises three phases. In the first phase, the input video is split into frames, and a distinct S-box for each frame is generated using the STS map. In the second step, we perform the frame rotation followed by the shuffling of pixels within the frame. Dynamic diffusion is the final step that changes the value of pixels by selecting one of the chaotic maps, either Chebyshev map or proposed STS map, for each frame. The security analysis proves that the proposed map is resistant to various attacks.
Spatial and temporal evolution of mine dust research: visual knowledge mapping analysis in Web of Science from 2001 to 2021

Dust pollution control is the basic guarantee of mine safety production, which has been widely concerned by scholars. Based on a total of 1786 publications collected by the Web of Science Core Collection (WOSCC) from 2001 to 2021, this paper analyzes the spatial-temporal distribution characteristics, hot topics, and frontier trends of the international mine dust field during the past 20 years by using Citespace and VOSviewer knowledge graph technology. The research shows that the study of mine dust can be divided into three stages: initial period (2001 ~ 2008), stable transition period (2009 ~ 2016), and boom period (2017 ~ 2021). The journals and disciplines which belong to mine dust research mainly focus on environmental science and engineering technology. A stable core group of authors and institutions have been preliminarily formed in the dust research field. The main themes of the study contained the whole process of mine dust generation, transport, prevention, and control, as well as the consequences of disaster. At present, the hot research fields mainly focus on mine dust particle pollution, multi-stage dust prevention, and emission reduction technologies, and mine occupational protection, monitoring, and early warning. In the future, the research should focus on the mechanism of dust production and transportation, the theory of efficient prevention and control, the technology and equipment of precise prevention and control of dust, and the high-precision monitoring and early warning of dust concentration. Future research should be concerned with dust control in underground mines and deep concave open-pit mines with complicated and treacherous environments, and strengthen research institutions, interdisciplinary cooperation, and interaction so as to promote the integration and application of mine dust and automation, information, and intelligent technology.
I Don’t Care How Popular You Are! Investigating Popularity Bias in Music Recommendations from a User’s Perspective

Recommender systems are designed to help us navigate through an abundance of online content. Collaborative filtering (CF) approaches are commonly used to leverage behaviors of others with a similar taste to make predictions for the target user. However, CF is prone to introduce or amplify popularity bias in which popular (often consumed or highly ranked) items are prioritized over less popular items. Many computational metrics of popularity biases — and resulting algorithmic (un)fairness — have been presented. However, it is largely unclear whether these metrics reflect human perception of bias and fairness. We conducted a user study with 170 participants to explore how users perceive recommendation lists created by algorithms with different degrees of popularity bias. Our results show — surprisingly — that popularity biases in recommendation lists are barely observed by users, even when corresponding bias/fairness metrics clearly indicate them.
Taking Search to Task

The importance of tasks in information retrieval (IR) has been long argued for, addressed in different ways, often ignored, and frequently revisited. For decades, scholars made a case for the role that a user's task plays in how and why that user engages in search and what a search system should do to assist. But for the most part, the IR community has been too focused on query processing and assuming a search task to be a collection of user queries, often ignoring if or how such an assumption addresses the users accomplishing their tasks. With emerging areas of conversational agents and proactive IR, understanding and addressing users' tasks has become more important than ever before. In this paper, we provide various perspectives on where the state-of-the-art is with regard to tasks in IR, what are some of the bottlenecks in deriving and using task information, and how do we go forward from here. In addition to covering relevant literature, the paper provides a synthesis of historical and current perspectives on understanding, extracting, and addressing task-focused search. To ground ongoing and future research in this area, we present a new framing device for tasks using a tree-like structure and various moves on that structure that allow different interpretations and applications. Presented as a combination of synthesis of ideas and past works, proposals for future research, and our perspectives on technical, social, and ethical considerations, this paper is meant to help revitalize the interest and future work in task-based IR.
Understanding Procedural Search Tasks “in the Wild”

People often search online for procedural (i.e., "how-to") knowledge. A procedural search task might involve a do-it-yourself project, cooking a dish, fixing a problem, or learning a new skill. Prior research has studied procedural search tasks from different perspectives: estimating the frequency of procedural searches online, understanding how people acquire procedural knowledge in specific contexts, and developing tools to support procedural search. Less research has aimed at deeply understanding procedural search tasks "in the wild". To bridge this gap, we conducted a survey (N = 128) on Amazon Mechanical Turk. Participants were asked to recall a recent procedural task for which they searched online. Participants were asked open-ended questions about the task itself and their unique situation (e.g., constraints and needs). Additionally, participants provided webpages they found useful in their searches and described the characteristics of the page that made it useful. Finally, they provided useful pieces of information from each selected page and explained what they gained from the information. Using an inductive coding approach, we analyzed participants' responses to gain insights about: (1) procedural task characteristics, (2) goals, (3) constraints, (4) contextual factors, (5) relevance criteria, and (6) gains obtained from useful information. Based on our results, we discuss important implications for future research and system design.
Text-Defend: Detecting Adversarial Examples using Local Outlier Factor

The problem of generating adversarial examples for text has been studied extensively; however, the detection of adversarial examples is largely underexplored. This paper studies the usage of Local Outlier Factors (LOF) to detect and filter adversarial examples from training data. Our experiments demonstrate that removing examples detected by LOF restores the performance of LSTM, CNN, and transformer-based classifiers on common sentence classification tasks. Our proposed technique outperforms DISP and FGWS, two state of the art detection techniques for identifying adversarial examples.
Simplified intensity- and phase-modulated transmitter for modulator-free decoy-state quantum key distribution

Quantum key distribution (QKD) allows secret key exchange between two users with unconditional security. For QKD to be widely deployed, low cost and compactness are crucial requirements alongside high performance. Currently, the majority of QKD systems demonstrated rely on bulk intensity and phase modulators to generate optical pulses with precisely defined amplitude and relative phase difference i.e., to encode information as signal states and decoy states. However, these modulators are expensive and bulky, thereby limiting the compactness of QKD systems. Here, we present and experimentally demonstrate a novel optical transmitter design to overcome this disadvantage by generating intensity- and phase-tunable pulses at GHz clock speeds. Our design removes the need for bulk modulators by employing directly modulated lasers in combination with optical injection locking and coherent interference. This scheme is, therefore, well suited to miniaturization and photonic integration, and we implement a proof-of-principle QKD demonstration to highlight potential applications.
Optimal (controlled) quantum state preparation and improved unitary synthesis by quantum circuits with any number of ancillary qubits

As a cornerstone for many quantum linear algebraic and quantum machine learning algorithms, controlled quantum state preparation (CQSP) aims to provide the transformation of $|i\rangle |0^n\rangle \to |i\rangle |\psi_i\rangle $ for all $i\in \{0,1\}^k$ for the given $n$-qubit states $|\psi_i\rangle$. In this paper, we construct a quantum circuit for implementing CQSP, with depth $O\left(n+k+\frac{2^{n+k}}{n+k+m}\right)$ and size $O\left(2^{n+k}\right)$ for any given number $m$ of ancillary qubits. These bounds, which can also be viewed as a time-space tradeoff for the transformation, are \optimal for any integer parameters $m,k\ge 0$ and $n\ge 1$. When $k=0$, the problem becomes the canonical quantum state preparation (QSP) problem with ancillary qubits, which asks for efficient implementations of the transformation $|0^n\rangle|0^m\rangle \to |\psi\rangle |0^m\rangle$. This problem has many applications with many investigations, yet its circuit complexity remains open. Our construction completely solves this problem, pinning down its depth complexity to $\Theta(n+2^{n}/(n+m))$ and its size complexity to $\Theta(2^{n})$ for any $m$. Another fundamental problem, unitary synthesis, asks to implement a general $n$-qubit unitary by a quantum circuit. Previous work shows a lower bound of $\Omega(n+4^n/(n+m))$ and an upper bound of $O(n2^n)$ for $m=\Omega(2^n/n)$ ancillary qubits. In this paper, we quadratically shrink this gap by presenting a quantum circuit of the depth of $O\left(n2^{n/2}+\frac{n^{1/2}2^{3n/2}}{m^{1/2}}\right)$.
Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques

Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining these data is crucial to find important underlying patterns. Classification is an effective method for identifying these patterns. This work proposes a deep investigation and analysis to evaluate and diagnose medical image data using various classification methods and to critically evaluate these methods’ effectiveness. The classification methods utilized include machine-learning (ML) algorithms like artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), logistic regression (LR), random subspace (RS), fuzzy logic and a convolution neural network (CNN) model of deep learning (DL). We applied these methods to two types of datasets: chest X-ray datasets to classify lung images into normal and abnormal, and melanoma skin cancer dermoscopy datasets to classify skin lesions into benign and malignant. This work aims to present a model that aids in investigating and assessing the effectiveness of ML approaches and DL using CNN in classifying the medical databases and comparing these methods to identify the most robust ones that produce the best performance in diagnosis. Our results have shown that the used classification algorithms have good results in terms of performance measures.
Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives

In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question–answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information.
A study on social media and higher education during the COVID-19 pandemic

Nowadays social media usage has increased drastically among the stakeholders of higher educational institutions. The COVID-19 pandemic has suddenly increased the surge of social media users due to the forced implementation of online pedagogy and travel restrictions. The research study presented in this paper attempted to analyze social media usage in higher education. The data were collected from primary and secondary sources with the help of leading research databases, survey questionnaires, the Delphi method, and brainstorming sessions. Statistical tools and analytic techniques incorporated in the study included bibliometric analysis, word cloud, co-occurrence network, thematic map, thematic evolution, co-word analysis, country-wise analysis along with collaboration network, statistical survey, mind mapping, and analytic hierarchy process. The study justified the aspects of social media usage in the higher educational environment. It was found that the research fraternity around the globe focused more on understanding the aspects of social media and higher education during the trying times of the Coronavirus crisis. The maximum impact of social media usage on higher education was found to be from teaching-learning and discussions, and public relations and networking. It was also found that social networking platforms like WhatsApp, YouTube, Facebook (Meta), LinkedIn, Instagram, and Twitter were very common among the stakeholders of higher education. This study is of huge importance because it can help in paving the way to strategize remedial measures for increasing positivity and minimizing the negativity of social media usage in institutions of higher education across the world.The online version contains supplementary material available at 10.1007/s10209-023-00988-x.
Hybrid Newton–Sperm Swarm Optimization Algorithm for Nonlinear Systems

Several problems have been solved by nonlinear equation systems (NESs), including real-life issues in chemistry and neurophysiology. However, the accuracy of solutions is highly dependent on the efficiency of the algorithm used. In this paper, a Modified Sperm Swarm Optimization Algorithm called MSSO is introduced to solve NESs. MSSO combines Newton’s second-order iterative method with the Sperm Swarm Optimization Algorithm (SSO). Through this combination, MSSO’s search mechanism is improved, its convergence rate is accelerated, local optima are avoided, and more accurate solutions are provided. The method overcomes several drawbacks of Newton’s method, such as the initial points’ selection, falling into the trap of local optima, and divergence. In this study, MSSO was evaluated using eight NES benchmarks that are commonly used in the literature, three of which are from real-life applications. Furthermore, MSSO was compared with several well-known optimization algorithms, including the original SSO, Harris Hawk Optimization (HHO), Butterfly Optimization Algorithm (BOA), Ant Lion Optimizer (ALO), Particle Swarm Optimization (PSO), and Equilibrium Optimization (EO). According to the results, MSSO outperformed the compared algorithms across all selected benchmark systems in four aspects: stability, fitness values, best solutions, and convergence speed.
Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the "stop-go" state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle's active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency.