Reviewerly/exHarmony huggingface dataset:
EWSO: Boosting White Shark Optimizer for solving engineering design and combinatorial problems

Population-based meta-heuristic algorithms are crucial for solving optimization issues. One of these recent algorithms that is now believed to be promising metaheuristic algorithm is the White Shark Optimizer (WSO). Although it has produced a number of encouraging results, it has some certain downsides like other metaheuristic algorithms (MAs). Dropping into the local minimum optima and local solution zones, the uneven distribution of exploration and exploitation abilities, and the slow rate of convergence are some of these downsides. To fight those, two efficient mechanisms, i.e., Enhanced Solution Quality (ESQ) and Orthogonal Learning (OL), have been applied to develop an enhanced version of WSO called EWSO. The effectiveness of EWSO has been comprehensively evaluated using the IEEE CEC'2022 test suite. For further verification and achieving the principle of generality, the proposed algorithm has been used to provide good solutions for three engineering design issues (i.e., Gear train, Vertical deflection of an I beam, and the piston lever), for further applicability it has also been employed to solve two combinatorial optimization problems (i.e., bin packing problem (BPP) and quadratic assignment problems (QAP)). This effectiveness has been evaluated compared to the most recent and common metaheuristics, i.e., Kepler Optimization Algorithm (KOA), Seagull Optimization Algorithm (SOA), Spider Wasp Optimizer (SWO), and some well-known metaheuristic algorithms such as; Sine cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Trees Social Relations Optimization (TSR), in addition to the original SWO. The experimental results and statistical measures confirm the effectiveness and reliability of the proposed algorithm (EWSO) in tackling real-world issues. It is able to overcome the previous drawbacks by providing the global optimum and preventing premature convergence through an increase in population diversity.
Deepfake detection using deep learning methods: A systematic and comprehensive review

Abstract Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL‐based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. This article is categorized under: Technologies &gt; Machine Learning Algorithmic Development &gt; Multimedia Application Areas &gt; Science and Technology
HIOS: Hierarchical Inter-Operator Scheduler for Real-Time Inference of DAG-Structured Deep Learning Models on Multiple GPUs

Neural-network-enabled data analysis in real-time scientific applications imposes stringent requirements on inference latency. Meanwhile, recent deep learning (DL) model design trends to replace a single branch with multiple branches for high prediction accuracy and robustness, which makes inter-operator parallelization become an effective approach to improve inference latency. However, existing inter-operator parallelization techniques for inference acceleration are mainly focused on utilization optimization in a single GPU. With the data size of an input sample and the scale of a DL model ever-growing, the limited resource of a single GPU is insufficient to support the parallel execution of large operators. In order to break this limitation, we study hybrid inter-operator parallelism both among multiple GPUs and in each GPU. In this paper, we design and implement a hierarchical inter-operator scheduler (HIOS) to automatically distribute large operators onto different GPUs and group small operators in the same GPU for parallel execution. Particularly, we propose a novel scheduling algorithm, named HIOS-LP, which consists of inter-GPU operator parallelization through iterative longest-path (LP) mapping and intra-GPU operator parallelization based on a sliding window. In addition to extensive simulation results, experiments with modern convolutional neural network benchmarks demonstrate that our HIOS-LP outperforms the state-of-the-art inter-operator scheduling algorithm IOS by up to 17% in real systems.
PELTA - Shielding Multiparty-FHE against Malicious Adversaries

Multiparty fully homomorphic encryption (MFHE) schemes enable multiple parties to efficiently compute functions on their sensitive data while retaining confidentiality. However, existing MFHE schemes guarantee data confidentiality and the correctness of the computation result only against honest-but-curious adversaries. In this work, we provide the first practical construction that enables the verification of MFHE operations in zero-knowledge, protecting MFHE from malicious adversaries. Our solution relies on a combination of lattice-based commitment schemes and proof systems which we adapt to support both modern FHE schemes and their implementation optimizations. We implement our construction in PELTA. Our experimental evaluation shows that PELTA is one to two orders of magnitude faster than existing techniques in the literature.
Lattice-Based Blind Signatures: Short, Efficient, and Round-Optimal

We propose a 2-round blind signature protocol based on the random oracle heuristic and the hardness of standard lattice problems (Ring/Module-SIS/LWE and NTRU) with a signature size of 20 KB. The protocol is round-optimal and has a transcript size that can be as small as 60 KB. This blind signature is around 4 times shorter than the most compact lattice-based scheme based on standard assumptions of del Pino and Katsumata (Crypto 2022) and around 2 times shorter than the scheme of Agrawal et al. (CCS 2022) based on their newly-proposed one-more-ISIS assumption. We also propose a "keyed-verification'' blind signature scheme in which the verifier and the signer need to share a secret key. This scheme has a smaller signature size of only 48 bytes, but further work is needed to explore the efficiency of its signature generation protocol.
Improved Distributed RSA Key Generation Using the Miller-Rabin Test

Secure distributed generation of RSA moduli (e.g., generating N=pq where none of the parties learns anything about p or q) is an important cryptographic task, that is needed both in threshold implementations of RSA-based cryptosystems and in other, advanced cryptographic protocols that assume that all the parties have access to a trusted RSA modulo. In this paper, we provide a novel protocol for secure distributed RSA key generation based on the Miller-Rabin test. Compared with the more commonly used Boneh-Franklin test (which requires many iterations), the Miller-Rabin test has the advantage of providing negligible error after even a single iteration of the test for large enough moduli (e.g., 4096 bits).
Implementation of Technology on English for Specific Purposes (ESP) Students in Communicative Language Teaching Approach

Students can prepare themselves to enter the global workforce by learning English for Specific Purpose (ESP) as communicative approach, which is a crucial part of the subject. One way to make learning ESP enjoyable for students so they will be driven to do and will be able to grasp the topic quickly is by using social media. This study aimed to analyze technology on English for specific purposes (ESP) students in communicative language teaching approach. This study applied qualitative method. The data collection techniques used are observation, interviews, and documentation. The triangulation technique as a data validity technique is carried out by comparing interview data with data obtained from observations and data from documentation. From the research conducted, it was found that the use of social media as a medium for learning ESP had a positive effect, namely, as a means of communication that is in high demand, as a tool for triggering student interaction and motivation, and as a collaborative tool. Implications for teaching ESP with the help of technology and suggestions for further research were also provided
A paradigm shift in Precambrian research driven by big data

The enigmatic nature of the Precambrian era poses ongoing challenges for researchers seeking to uncover its secrets. With the exponential growth of scientific data, there exists a vast repository of information through which these mysteries can be explored. However, this data is often derived from fragmented investigations of geological phenomena within specific disciplinary domains and limited spatiotemporal boundaries, resulting in untapped potential for extracting undiscovered knowledge. To address this limitation, the field of big data science provides a foundation for multidisciplinary research on the geological evolution of the Precambrian. By harnessing the power of large-scale data integration and analysis, valuable insights into this pivotal era in Earth's history can be revealed. This paper offers a comprehensive overview of big data types in geoscience and elucidates common analysis methods. Through a case study focused on the North China Craton (NCC), we demonstrate the application of conjoint analysis to a dataset comprising rock and mineral geochemistry data. Employing local singularity analysis and wavelet analysis on zircon age frequency and Hf isotopic time series, we reveal a persistent long-term periodicity of 800–500 million years since 3.5 billion years ago. This finding indicates a co-evolution between the NCC and the global supercontinent cycle dating back to the Archean period. To investigate the formation of the NCC, we employ machine learning-based crustal thickness evolution reconstruction, which highlights arc formation during subduction as the primary factor, with regional mantle activities playing a secondary role in the convergence. Combining spatio-temporal evolution analysis of magmatic intensity and εHf(t) values, we infer that the development of the NCC primarily resulted from a prolonged process of accretion targeting the Eastern Block (the primary continent nuclei) through the incorporation of diverse arc massifs. This examination of the NCC serves as an example of how data collection, processing, and utilization can enhance our understanding of formational and evolutionary processes, signaling a paradigm shift in Precambrian research driven by the integration of big data.
Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption

Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate activation maps and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing activation maps could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies homomorphic encryption on the activation maps before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved.
A trusted medical data sharing framework for edge computing leveraging blockchain and outsourced computation

Traditional cloud-centric approaches to medical data sharing pose risks related to real-time performance, security, and stability. Medical and healthcare data encounter challenges like data silos, privacy breaches, and transmission latency. In response to these challenges, this paper introduces a blockchain-based framework for trustworthy medical data sharing in edge computing environments. Leveraging healthcare consortium edge blockchains, this framework enables fine-grained access control to medical data. Specifically, it addresses the real-time, multi-attribute authorization challenge in CP-ABE through a Distributed Attribute Authorization strategy (DAA) based on blockchain. Furthermore, it tackles the key security issues in CP-ABE through a Distributed Key Generation protocol (DKG) based on blockchain. To address computational resource constraints in CP-ABE, we enhance a Distributed Modular Exponentiation Outsourcing algorithm (DME) and elevate its verifiable probability to "1". Theoretical analysis establishes the IND-CPA security of this framework in the Random Oracle Model. Experimental results demonstrate the effectiveness of our solution for resource-constrained end-user devices in edge computing environments.
Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators

In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.
Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.
A Graph-Based Approach to Generate Energy-Optimal Robot Trajectories in Polygonal Environments

As robotic systems continue to address emerging issues in areas such as logistics, mobility, manufacturing, and disaster response, it is increasingly important to rapidly generate safe and energy-efficient trajectories. In this article, we present a new approach to plan energy-optimal trajectories through cluttered environments containing polygonal obstacles. In particular, we develop a method to quickly generate optimal trajectories for a double-integrator system, and we show that optimal path planning reduces to an integer program. To find an efficient solution, we present a distance-informed prefix search to efficiently generate optimal trajectories for a large class of environments. We demonstrate that our approach, while matching the performance of RRT* and Probabilistic Road Maps in terms of path length, outperforms both in terms of energy cost and computational time by up to an order of magnitude.
Cryptanalysis of Random Affine Transformations for Encrypted Control

Cloud-based and distributed computations are of growing interest in modern control systems. However, these technologies require performing computations on not necessarily trustworthy platforms and, thus, put the confidentiality of sensitive control-related data at risk. Encrypted control has dealt with this issue by utilizing modern cryptosystems with homomorphic properties, which allow a secure evaluation at the cost of an increased computation or communication effort (among others). Recently, a cipher based on a random Affine transformation gained attention in the encrypted control community. Its appeal stems from the possibility to construct security providing homomorphisms that do not suffer from the restrictions of "conventional" approaches. This paper provides a cryptanalysis of random Affine transformations in the context of encrypted control. To this end, a deterministic and probabilistic variant of the cipher over real numbers are analyzed in a generalized setup, where we use cryptographic definitions for security and attacker models. It is shown that the deterministic cipher breaks under a known-plaintext attack, and unavoidably leaks information of the closed-loop, which opens another angle of attack. For the probabilistic variant, statistical indistinguishability of ciphertexts can be achieved, which makes successful attacks unlikely. We complete our analysis by investigating a floating point realization of the probabilistic random Affine transformation cipher, which unfortunately suggests the impracticality of the scheme if a security guarantee is needed.
Noah's Ark: Efficient Threshold-FHE Using Noise Flooding

We outline a secure and efficient methodology to do threshold distributed decryption for LWE based Fully Homomorphic Encryption schemes. Due to the smaller parameters used in some FHE schemes, such as Torus-FHE (TFHE), the standard technique of "noise flooding'' seems not to apply. We show that noise flooding can also be used with schemes with such small parameters, by utilizing a switch to a scheme with slightly higher parameters and then utilizing the efficient bootstrapping operations which TFHE offers. Our protocol is proved secure via a simulation argument, making its integration in bigger protocols easier to manage.
Trivial Transciphering With Trivium and TFHE

We examine the use of Trivium and Kreyvium as transciphering mechanisms for use with the TFHE FHE scheme. Trivium was introduced in the eSTREAM project as a general purpose stream cipher, whilst Kreyvium was introduced to strengthen Trivium (in the context of transciphering BGV/BFV ciphertext). Previously both ciphers were investigated for FHE transciphering only in the context of the BGV/BFV FHE schemes; this is despite Trivium and Kreyvium being particularly suited to TFHE. Recent work by Dobraunig et al. gave some initial experimental results using TFHE. We show that these two symmetric ciphers have excellent performance when homomorphically evaluated using TFHE. Indeed we improve upon the results of Dobraunig et al. by at least two orders of magnitude in terms of latency. This shows that, for TFHE at least, one can transcipher using a standardized symmetric cipher (Trivium), without the need for special FHE-friendly ciphers being employed. For applications wanting extra security, but without the benefit of relying on a standardized cipher, our work shows that Kreyvium is a good candidate.
A Homomorphic AES Evaluation in Less than 30 Seconds by Means of TFHE

Since the pioneering work of Gentry, Halevi, and Smart in 2012 \citegentry_BGV, the state of the art on transciphering has moved away from work on AES to focus on new symmetric algorithms that are better suited for a homomorphic execution. Yet, with recent advances in homomorphic cryptosystems, the question arises as to where we stand today. Especially since AES execution is the application that may be chosen by NIST in the FHE part of its future call for threshold encryption. In this paper, we propose an AES implementation using TFHE programmable bootstrapping which runs in less than a minute on an average laptop. We detail the transformations carried out on the original AES code as well as the optimized FHE operators we developed to lead to a more efficient homomorphic evaluation. We also duly give several execution times on different machines, depending on the type of execution (sequential or parallelized). These times vary from 4.5 minutes (resp. 54 secs) for sequential (resp. parallel) execution on a standard laptop down to 28 seconds for a parallelized execution over 16 threads on a multi-core workstation.
Detection and depth estimation for domestic waste in outdoor environments by sensors fusion

In this work, we estimate the depth in which domestic waste are located in space from a mobile robot in outdoor scenarios. As we are doing this calculus on a broad range of space (0.3 - 6.0 m), we use RGB-D camera and LiDAR fusion. With this aim and range, we compare several methods such as average, nearest, median and center point, applied to those which are inside a reduced or non-reduced Bounding Box (BB). These BB are obtained from segmentation and detection methods which are representative of these techniques like Yolact, SOLO, You Only Look Once (YOLO)v5, YOLOv6 and YOLOv7. Results shown that, applying a detection method with the average technique and a reduction of BB of 40%, returns the same output as segmenting the object and applying the average method. Indeed, the detection method is faster and lighter in comparison with the segmentation one. The committed median error in the conducted experiments was 0.0298 ± 0.0544 m.