Density peaks clustering (DP) has the ability of detecting clusters of arbitrary shape and clustering non-Euclidean space data, but its O(n 2) complexity in both computing and storage makes it difficult to scale for big data. Various approaches have been proposed in this regard, including MapReduce based distribution computing, multi-core parallelism, presentation transformation (e.g., kd-tree, Z-value), granular computing, and so forth. However, most of these existing methods face two limitations. One is their target datasets are mostly constrained to be in Euclidian space, the other is they emphasize only on local neighbors while ignoring global data distribution due to restriction to cutoff kernel when computing density. To address the two issues, we present a faithful and parallel DP method that makes use of two types of vector-like distance matrices and an inverse leading-node-finding policy. The method is implemented on a message passing interface (MPI) system. Extensive experiments showed that our method is capable of clustering non-Euclidean data such as in community detection, while outperforming the state-of-the-art counterpart methods in accuracy when clustering large Euclidean data. Our code is publicly available at https://github.com/alanxuji/FaithPDP.

Object manipulation for construction assembly using a crane is a control problem with highly challenging dynamics, merging contact-rich manipulation, high dynamics uncertainty from the outdoors environment, coarse actuators, and an underactuated system. The inevitable contact from assembly presents both a potential to exacerbate small errors as well as an opportunity to limit uncertainty by restricting the possible configuration space. For a controller to take advantage of this beyond simple lift-up and lay-down operations, a system that is capable of considering how uncertainty evolves under contact dynamics is required. We approach this problem by learning the dynamics of the payload’s possible occupancy distribution using a visual foresight inspired model, then performing learning-based model predictive control with this learned model as the dynamics model, with the objective of creating possible occupancy that corresponds to one payload instance in its target position. We evaluate this system in simulation on the problem of I-beam assembly, specifically, aligning and inserting a horizontal box member between flanges of two opposing vertical I-beams.  

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications, where a stable communication link is vital for safe flight control and robust data transmission. To develop a reliable UAV communication system, it is necessary to deeply understand the UAV channel characteristics and establish accurate channel models. Channel sounding is the most effective way to obtain realistic channel characteristics and validate the theoretical channel model. However, the studies on UAV channel sounding are still insufficient in terms of system design and data processing due to the complexity of developing a UAV channel sounder. Different from the terrestrial channel sounders, the implementation of a UAV channel sounder is tortured by the limited battery life and payload capacity of the UAV platform. The sounding scheme and data post-processing also need to be specially designed for highly dynamic UAV channels. So far, most existing survey studies on UAV channels focus on modeling methodology and model presentation. To fill this gap, this paper provides a comprehensive survey on the design of the UAV channel sounder, in terms of the hardware scheme, sounding signal, time synchronization, calibration, and data postprocessing. Current issues and potential research topics behind existing sounding technologies and measurement campaigns are analyzed. Moreover, future challenges and open issues are also discussed.

Scalability, heterogeneity, energy efficiency, costeffectiveness, robustness, interoperability, and low latency data transfer are some of the critical challenges posed by the Internet of Things in the modern era of the Internet. Content Centric Networks (CCN) and Named Data Networks (NDN) are some proposed solutions that can meet the abovementioned challenges. In-network caching, multicasting, content security, and decoupling of data from location are the significant advantages offered by the CCN. Despite the diverse benefits of CCN, it has to face some significant problems, such as how the key management and distribution are performed among the consumers and key managers efficiently, how the nodeto-node communication occurs, and how to provide services to the neighboring nodes while saving resources, etc. This paper proposes an Elliptic Curve Cryptography-based secure communication protocol supporting broadcast/multicast IoT data. Push-based data generation and transfer, typical requirements of IoT devices, have been integrated with the proposed protocol. The proposed communication protocol has been compared with existing protocols regarding computation and communication overheads as well as functional and security features. The proposed protocol has been evaluated in AVISPA and NS3 simulators for security and efficiency, and it is found that the proposed protocol outperforms the existing protocols in all aspects.

Terahertz (THz) communication is considered one of the most critical technologies for 6G because of its abundant bandwidth. To compensate the high propagation of THz, analog/digital hybrid precoding for THz massive multiple input multiple output (MIMO) is proposed to focus signals and extend communication range. Notably, considering hardware cost and power consumption, infinite and high-resolution phase shifters (PSs) are difficult to implement in THz massive MIMO and lowresolution PSs are typically adopted in practice. However, lowresolution PSs cause severe performance degradation. Moreover, the beam squint in wideband THz massive MIMO increases the performance degradation because of the frequency independence of the analog PSs. Motivated by the above factors, in this paper, we firstly propose a heuristic algorithm under fully connected (FC) structure, which optimize the digital precoder and the analog precoder alternately. Then we migrate the proposed heuristic algorithm to the partially-connected (PC) architecture. To further improve the performance, we extend our design to dynamic subarrays in which each RF chain is connected to any antenna that does not duplicate. The numerical results demonstrate that our proposed wideband hybrid precoding with low-resolution PSs achieves better performance to the comparisons for both FC structure and PC structure.

In this work, we propose the first hardware implementation of Classic McEliece protected with countermeasures against Side-Channel Attacks (SCA) and Fault Injection Attacks (FIA). Classic Mceliece is one of the leading candidates for Key Encapsulation Mechanisms (KEMs) in the ongoing round 4 of the NIST standardization process for post-quantum cryptography. In particular, we implement a range of generic countermeasures against SCA and FIA, particularly protected the vulnerable operations such as additive Fast Fourier Transform (FFT) and Gaussian elimination, that have been targeted by prior SCA and FIA attacks. We also perform a detailed SCA evaluation demonstrating no leakage even with 100000 traces (improvement of more than 100× the number of traces compared to unprotected implementation). This comes at a modest total area overhead of between 4× to 7×, depending on the type of implemented SCA countermeasure. Furthermore, we present a thorough ASIC benchmark for SCA and FIA protected Classic McEliece design.

We consider the effectiveness of multi-objective counterfactual explanations (MOCE) in helping individuals learn tactics, or rules of thumb, to apply when required to select a course of action in a specific context. In this setting, a counterfactual explanation compares one course of action against another. A MOCE presents this comparison by highlighting how the two options differ across a range of objectives or metrics. We conduct a study in which participants are presented with various scenarios, alongside courses of action that could be implemented in those scenarios. Counterfactual explanations, including those involving multiple objectives, are used to identify the positive and negative aspects of the provided options. Participants were then required to identify the best course of action in a range of contexts. Participants trained with MOCE outperformed those given no explanations in seven of eight scenarios and those given single-objective explanations (SOCE) in four. SOCEs gave participants an aggregated outcome (expected rewards) without breaking these into specific objectives. MOCE improved tactic learning, but participants provided with SOCE or no explanation performed better in multi-tactic scenarios. These findings suggest that MOCE enhances tactical decision-making, but further research is needed for multi-tactic integration.

Bit manipulations, especially those executed on multiple strings in parallel, e.g., on Intel® processors equipped with Advanced Vector Extensions (AVX), can be a powerful way to speed up unoptimized high-level sequentially executed code.  A case in point is made for floating-point arithmetic coding (FPAC), implemented herein as a non-adaptive, lossless data compression algorithm using x86 AVX-256 stand-alone assembly language under 64-bit MASM assembler in Visual Studio 2022.  Apart from writing and reading bit strings to and from file, FPAC can become fully vectorized and be improved in performance (relative to unoptimized integer versions) by orders of magnitude by blocking short sequences of symbols and bypassing interval renormalization.  For an alphabet size up to 53—the limiting case made for 0.474MB Protein Data Bank entry 4HHB, referred to as oxygen transport file (OTF)—it can also strongly outperform a commercially available, unoptimized C++ Huffman encoder by over a factor of 10 and beat the decoder by roughly a factor of 2.  Disadvantageous but necessary to this prescription of vectorizations is an additional compressed storage requirement of the length of the codeword (this binary integer is to encode a block of 5 symbols) in addition to the codeword itself;  for size-5 blocks, this compromises the compression efficiency as follows: the average number of bits per symbol required to compress the input message is demonstrated to lie in the interval [H(S) + b, H(S) + 0.4 + b], where b = 1.0 for single-precision floating-point arithmetic coding (SPFPAC), b = 1.2 for a slower but more practical double-precision counterpart (DPFPAC), and H(S) is the Shannon entropy of symbol frequencies. 

Integrating radar and communication systems for economical use of hardware and spectrum resources is projected to be a crucial aspect of sixth-generation (6G) systems, leading to extensive research in the integrated sensing and communication (ISAC) area. In this article, we propose a radar detector structure for a multiple-input multiple-output (MIMO) ISAC system using orthogonal frequency-division multiplexing (OFDM) modulated waveforms. These waveforms are utilized to communicate with downlink (DL) users while receiving echoes from targets and clutter, in addition to separate OFDM-modulated uplink (UL) communication waveforms. The transmitter (Tx) and receiver (Rx) employ hybrid beamformers, with analog beamformers precisely designed to cover flat angular sectors. Tx and Rx beams are directed towards DL and UL users, respectively, with the possibility of overlapping or separate angular sectors. We introduce a Doppler-aware Code Bank (DACB) as the initial processing stage, thoroughly investigating the effects of Doppler mismatch. Following DACB, a sub-optimal sequential angle-range processing (SARP) method is proposed to maximize the output signal-to-interference-plus-noise ratio (SINR) while maintaining feasible processing. A two-variant detector scheme is proposed to address this processing's suboptimality. Four different detectors with varying complexities, including a fully adaptive detector, are introduced. The potential beam-squint effect due to increased bandwidth is also considered, and a subband approach is proposed to mitigate these effects. Simulation results for all four detectors, as well as a conventional 3-dimensional periodogram detector commonly used as a benchmark in the literature, are provided. The results demonstrate that the proposed detectors can significantly enhance SINR and the probability of detection, particularly when accounting for the coupling between the clutter Doppler and Tx OFDM symbols.

In today's era of digitalization, streamlining traffic management and ensuring public safety are paramount. Traditional methods of driver license verification often face challenges in terms of efficiency, accuracy, and real-time capabilities. To address these limitations, we propose a novel Driving License Verification System (DLVS) that leverages advanced facial recognition and optical flow techniques to revolutionize driver license verification. The DLVS seamlessly integrates with traffic management systems, enabling real-time verification of driver licenses, enhancing traffic communication, and promoting overall public safety.

Reduction of transport emissions, including critical infrastructures like airports, is a consistent part of the European political goals. Introduction of local renewable energy sources into a complicated energy system as a modern airport requires dedicated optimisation tools and methods. The present paper presents results of comparing two modelling tools OSeMOSYS and Integrate applied for configuration of two case airports: Torino (Italy) and Stavanger (Norway) respectively. The results outline optimal paths for sustainable decarbonisation creating integrated energy systems, which rely on utilisations of locally available resources as PV, Wind and biomethane or deployment of a local hydrogen network, the latter a fundamental enabler for a long-term strategy based on hydrogen. The study highlights the importance of dedicated modelling tools for planning and operation phases for integrated energy systems.

Instagram is a popular social media platform among youth, with over a billion active users worldwide. The platform allows users to share photos, videos, and stories and connect with friends and followers. Instagram has become a significant part of youth culture, with many young people spending a considerable amount of time on the app. According to Enterprise (2012:02), Instagram is a community shared among one member with other members from all over the world. This study examines the impact of Instagram on the physical and mental wellbeing of Indian youth. With the rapid proliferation of social media platforms in India, Instagram has emerged as a prominent influence on the lives and perceptions of young individuals. Studies have shown that using Instagram among youth can have positive and negative effects. On the positive side, Instagram can be a way for young people to express themselves, connect with others, and discover new interests. However, excessive use of the platform can also lead to negative outcomes, such as social comparison, anxiety, and low self-esteem. Instagram addiction among youth is a growing concern. Some young people may feel compelled to check their accounts frequently and may experience negative emotions if they are unable to do so. In extreme cases, excessive use of the platform can interfere with daily activities and relationships, leading to a negative impact on mental health. Parents and educators can play a role in helping young people manage their Instagram use. This can include setting limits on screen time, encouraging healthy online habits, and promoting offline activities and social interaction. Additionally, mental health professionals can offer support to young people who are struggling with addiction or other negative effects of Instagram use. This study explores the prevalence of Instagram dependency among Indian youth.

In electrical engineering, electric charges usually move at speeds substantially slower than the speed of light. Therefore, relativistic mechanics and the Lorentz transformation are rarely applied; instead, the much simpler Newtonian mechanics and the Galilean transformation are used. This approximation is hoped to yield useful results. In this article, the exact solution of Maxwell's equations for arbitrarily moving point charges is used to demonstrate that this approximation is not suitable for point charges, even if they move extremely slowly. Nevertheless, in electrical engineering, great practical demand exists for a functioning and consistent electrodynamic model for non-relativistic point charges. This article presents such a model and demonstrates that it ensures the universal constancy of the speed of light for all receiving antennas, while satisfying the Galilean principle of relativity regarding the point charges, and being compatible with Newtonian mechanics and the classical Newtonian conservation of momentum. The framework can describe electromagnetic waves as well as all static and quasistatic effects with excellent quality if they are generated by non-relativistic point charges. The framework is based on the exact solution of Maxwell's equations for arbitrarily moving point charges and a simplified Lorentz force law, which has been calibrated so that the force for slow velocities matches Ampère's original force law and thus fulfills the predictions of magnetostatics and quasistatics.

Air pollution, a silent public health crisis, demands smarter solutions. This research explores a novel approach: using machine learning and the Internet of Things (IoT) to predict hyper-local Air Quality Index (AQI). We integrate a Random Forest algorithm with NodeMCU for pinpoint-accurate AQI forecasts. The core lies in a robust machine learning model trained on historical data rich in environmental parameters-air quality indicators like PM2.5 and ozone, coupled with meteorological factors. This empowers the model to predict future AQI levels with exceptional accuracy. Real-time data acquisition is handled by NodeMCU, an IoT platform acting as our environmental guardian. It gathers live data and feeds it to the model, enabling real-time, location-specific AQI predictions. The model's effectiveness is rigorously evaluated, achieving a promising 99% accuracy-a testament to its potential to revolutionize air quality management. By harnessing the power of machine learning and IoT, this research paves the way for a future where proactive air quality management fosters healthier cities and a more sustainable future.

Interactive art often struggles to dynamically reflect audience emotions in real-time. To address this, we developed Expressive Canvas, a system that integrates emotion detection with real-time image manipulation. This innovation aims to transform audience emotions into dynamic visual art, creating immersive experiences. Our approach employs a pose-to-emotion detection model using various mathematical equations to enhance accuracy. Trained on the FER-2013 dataset, our model achieved a test accuracy of 62.47%, despite significant challenges arising from class imbalances, particularly for the "Disgust" emotion. The system performs efficiently, detecting emotions in an average of 8.43 seconds and manipulating images in just 0.02 seconds. Field tests of Expressive Canvas across various events demonstrated its consistent performance and reliability. Despite existing challenges, the system successfully transformed audience emotions into visual expressions, enhancing the connection between art and viewers. This study highlights the potential for real-time emotional expression in interactive art. Future work will focus on mitigating class imbalances and optimizing the neural network to improve accuracy and expressiveness. Key terms include emotion detection, pose-to-emotion detection, real-time image manipulation, interactive art, and neural networks.

In today's era websites have emerged as a means for businesses and organizations to connect with their customers. To ensure a user experience, across devices such as desktops, laptops, smartphones and tablets it is essential for a website to be both responsive and visually appealing. This survey paper aims to provide an overview of the methods recommended approaches and current patterns in designing and developing websites that're both responsive and visually captivating. The objective of this paper is to equip web developers, designers and researchers, with an understanding of the process involved in creating websites that're both responsive and visually appealing. As digital technologies continue to advance it is becoming increasingly vital to create engaging web experiences that cater to the user's needs.

Terahertz (THz) communications is an emerging technology for sixth generation (6G) wireless communication systems and beyond with many challenges. Reaching Terabits per second (Tpbs) channel capacity in THz-band is physically crucial to the number of antennas at the base station (BS) and reconfigurable intelligent surface (RIS) elements, and requires careful innovative planning to manipulate electromagnetic (EM) waves. Throughout this letter, we propose joint beamforming (BF) and reflecting optimization for maximizing downlink (DL) data rate in THz-band communications equipped with RIS-aided orthogonal frequency division multiple access (OFDMA) and ultra-massive multiple-input multiple-output (UM-MIMO) system. Based on UM-MIMO channel model we derive, a non-convex problem is formulated in order to maximize the channel DL data rate. Then, we propose an alternating iterative algorithm to jointly optimize the BS BF weights, and RIS phase shift coefficients. In particular, the BS BF is solved by the gradient descent (GD) algorithm, while the RIS phase shift coefficients are solved by successive convex approximation (SCA) algorithm. Monte-Carlo simulation results are carried out to verify the efficiency and performance of the proposed framework and reveal that the DL data rate is significantly can be maximized.

The proliferation of advanced natural language processing applications has demonstrated the critical importance of ensuring the accuracy and reliability of generated text, particularly in domains where erroneous information can have significant consequences. The novel integration of a majority voting mechanism within a Mixture of Experts (MoE) framework, specifically in the Mistral 8x7b model, offers a significant advancement by leveraging the collective expertise of multiple specialized experts to mitigate hallucinations and enhance output precision. Through a rigorous experimental setup, the research demonstrated that the application of majority voting substantially reduced the incidence of hallucinations while improving the overall accuracy of the model’s outputs. Detailed analysis revealed that the consensus-based approach effectively filtered out erroneous responses, providing a more reliable and trustworthy output. The methodology encompassed comprehensive mechanisms for input processing, expert output collection, and dynamic expert selection, further refining the model’s contextual adaptability and robustness. Results indicated a notable improvement in accuracy and a manageable increase in computational overhead, validating the practical viability of the approach. This study’s findings significantly contribute to the ongoing efforts to enhance the reliability of large language models, presenting a robust framework with broad applications in various critical fields.