Reviewerly/exHarmony huggingface dataset:
Scalable and Accelerated Self-healing Control Circuit Using Evolvable Hardware

Authors:
raw_author_name: Noor Mahammad.Sk
raw_affiliation_string: Indian Institute of Information Technology, Design and Manufacturing, India

Abstract: Controllers are mission-critical components of any electronic design. By sending control signals, they decide which and when other data path elements must operate. Faults, especially Single Event Upset (SEU) occurrence in these components, can lead to functional/mission failure of the system when deployed in harsh environments. Hence, competence to self-heal from SEU is highly required in the control path of the digital system. Reconfiguration is critical for recovering from a faulty state to a non-faulty state. Compared to native reconfiguration, the Virtual Reconfigurable Circuit (VRC) is an FPGA-generic reconfiguration mechanism. The non-partial reconfiguration in VRC and extensive architecture are considered hindrances in extending the VRC-based Evolvable Hardware (EHW) to real-time fault mitigation. To confront this challenge, we have proposed an intrinsic constrained evolution to improve the scalability and accelerate the evolution process for VRC-based fault mitigation in mission-critical applications. Experimentation is conducted on complex ACM/SIGDA benchmark circuits and real-time circuits used in space missions, which are not included in related works. In addition, a comparative study is made between existing and proposed methodologies for brushless DC motor control circuits. The hardware utilization in the multiplexer has been significantly reduced, resulting in up to a 77% reduction in the existing VRC architecture. The proposed methodology employs a fault localization approach to narrow the search space effectively. This approach has yielded an 87% improvement on average in convergence speed, as measured by the evolution time, compared to the existing work.
Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers

Authors:
raw_author_name: Allen G. Hart
raw_affiliation_string: University of Bath, Claverton Down, Bath, BA2 7AY, UK

Abstract: We establish conditions under which a continuous time reservoir computer, such as a leaky integrator echo state network, admits a generalised synchronisation f between the source dynamics and reservoir dynamics. We show that multiple generalised synchronisations can exist simultaneously, and connect this to the multi-Echo-State-Property (multi-ESP). In the special case of a linear reservoir computer, we derive a closed form expression for the generalised synchronisation f. Furthermore, we establish conditions under which f is of class C1, and conditions under which f is a topological embedding on the fixed points of the source system. This embedding result is closely related to Takens' embedding Theorem. We also prove that the embedding of fixed points occurs almost surely for randomly generated linear reservoir systems. With an embedding achieved, we discuss how the universal approximation theorem makes it possible to forecast the future dynamics of the source system and replicate its topological properties. We illustrate the theory by embedding a fixed point of the Lorenz-63 system into the reservoir space using numerical methods. Finally, we show that if the observations are perturbed by white noise, the GS is preserved up to a perturbation by an Ornstein–Uhlenbeck process.
An anti-steganalysis adaptive steganography for HEVC video based on PU partition modes

Authors:
raw_author_name: Songhan He
raw_affiliation_string: College of Information Science and Engineering, Ningbo University, Ningbo 315211, China; School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China
raw_author_name: Dawen Xu
raw_affiliation_string: School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China
raw_author_name: Lin Yang
raw_affiliation_string: College of Information Science and Engineering, Ningbo University, Ningbo 315211, China; School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China
raw_author_name: Haojun Dai
raw_affiliation_string: College of Information Science and Engineering, Ningbo University, Ningbo 315211, China; School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China
raw_author_name: Shanshan Wang
raw_affiliation_string: School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China

Abstract: Calibration-based steganalysis methods are a fatal threat to current HEVC steganography algorithms. The current steganography algorithms based on prediction unit (PU) still lack resistance to this type of steganalysis. The reason is that the improvements in HEVC codec provide more potential steganographic space as well as introduce more detectable distortion. Therefore, a HEVC video steganography method that has resistance to PU shift-based steganalysis is proposed in this paper. First, the PU shift phenomenon that exists in PU-based steganography is introduced. Then the existing calibration-based steganalysis method is enhanced. According to the encoding rules of HEVC, the distortion cost function according to the difference of rate distortion cost (RD cost) during recompression is designed, which aims to obtain better resistance to calibration-based steganalysis. Experimental results demonstrate that the steganography algorithm designed in this paper provides excellent resistance ability for calibration-based steganalysis.
CTFCD: Channel transformer based on full convolutional decoder for single image deraining

Authors:
raw_author_name: Shaohan Tan
raw_affiliation_string: College of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, China
raw_author_name: Hui Chen Songhao Zhu
raw_affiliation_string: College of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, China

Abstract: Although convolutional neural network and visual transformer have been successfully applied in various field of computer vision, there is little work combining them to construct an efficient network model to solve image deraining tasks. Convolutional neural network is utilized to extract the features from each region, while visual transformer is utilized to extract the context information between local features. Due to the limitations in computational resources and processing time, it is difficult for visual transformer to process high-resolution images, which hinders the application of visual transformer in devices with limited hardware resources. The purpose of this article is to utilize the advantages of to design a lightweight encoder-decoder network for real-time image deraining. Firstly, a novel channel Transformer module is designed to obtain global contextual information, where deep separable convolution is utilized to extract multi-scale local features and a Transformer encoder is constructed by stacking Transformer modules. Secondly, a decoder based on a fully convolution is designed to adopt mask attention and inverted bottleneck convolution to achieve progressive feature fusion and feature reconstruction, which significantly reduces computational complexity and memory requirement. A large number of experimental results have verified that the proposed method has superior performance compared with other state-of-the art methods, while the computational cost and parameter quantity are much smaller than those of similar methods.
YembaTones: A syllable-tone annotated dataset for speech recognition and prosodic analysis of the Yemba language

Authors:
raw_author_name: Paulin Melatagia Yonta
raw_affiliation_string: Department of Computer Science, University of Yaounde I, Yaounde, Cameroon; IRD, UMMISCO, F-93143, Bondy, France

Abstract: Prosody is a key area of linguistics that explores tonal and rhythmic variations in speech. In tonal languages such as Yemba, prosody plays a crucial role in distinguishing between words with different meanings or different grammatical forms. However, despite the large number of native speakers of this language in Cameroon, there are few resources for the speech recognition and synthesis. In this article, we present YembaTones, a syllabic and tonal annotated dataset, created from a dictionary we designed of 344 Yemba/French words coming from the most common phrases of the language, grouped according to their spellings that only differ by the tone. The dataset was originally designed for training and evaluating tone detection models for tonal and low resource languages. The recordings of the pronunciation of these words were made with 11 native speakers of Yemba, mainly specialists in linguistics with a good command of the sounds of the language. The recordings were made with a dictaphone in different places such as the homes of the speakers, the campuses and their workplaces. Then they have been cleaned and segmented into individual audio files corresponding to the pronunciations of isolated words, using the software Audacity. After cleaning and segmentation, we selected 3420 good quality audio files for annotation. Annotations were made at the syllabic and tonal level using Praat software. YembaTones is a valuable resource not only for the training and evaluation of automatic tone detection models but also for automatic speech recognition, speech synthesis of tonal and poorly endowed languages, as well as for the study of prosody and Yemba phonetics, research in speech acoustics and phonetic linguistics.
Algorithms for 2-club cluster deletion problems using automated generation of branching rules

Authors:
raw_author_name: Dekel Tsur
raw_affiliation_string: Ben-Gurion University of the Negev, Israel

Abstract: In the 2-Club Cluster Vertex Deletion (resp., 2-Club Cluster Edge Deletion) problem the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k vertices (resp., edges) whose removal from G results in a graph in which the diameter of every connected component is at most 2. In this paper we give algorithms for 2-Club Cluster Vertex Deletion and 2-Club Cluster Edge Deletion whose running times are O⁎(3.104k) and O⁎(2.562k), respectively. Our algorithms were obtained using automated generation of branching rules. Our results improve the previous O⁎(3.303k)-time algorithm for 2-Club Cluster Vertex Deletion [Liu et al., FAW-AAIM 2012] and the O⁎(2.695k)-time algorithm for 2-Club Cluster Edge Deletion [Abu-Khzam et al., TCS 2023].
Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments

Authors:
raw_author_name: Hyeonmo Kim
raw_affiliation_string: Department of Computer Engineering, Sejong University, Seoul, Republic of Korea
raw_author_name: Hanju Kim
raw_affiliation_string: Department of Computer Engineering, Sejong University, Seoul, Republic of Korea
raw_author_name: Myungha Cho
raw_affiliation_string: Department of Computer Engineering, Sejong University, Seoul, Republic of Korea
raw_author_name: Bay Vo
raw_affiliation_string: Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Viet Nam
raw_author_name: Jerry Chun-Wei Lin
raw_affiliation_string: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Poland
raw_author_name: Hamido Fujita
raw_affiliation_string: Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Spain; Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia; Regional Research Center, Iwate Prefectural University, Iwate, Japan
raw_author_name: Unil Yun
raw_affiliation_string: Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

Abstract: Various studies on high utility pattern mining have been conducted to satisfy the emerging need to consider the characteristics of real-world databases, such as the importance and quantity of items. In the traditional utility-based framework, the mining result is influenced by the number of items in a pattern, or in some cases, single utilities of items. In order to overcome this drawback, high average utility pattern mining has been proposed. It provides more interesting results since it takes into account the average utility of patterns by considering their lengths. Methods based on this concept have emerged in recent years, including ones that target incremental environments. However, existing algorithms create an enormous number of candidate patterns or require complex operations during the mining process. To address this degradation, we propose a new and more efficient approach for mining high average utility patterns in dynamic environments. The proposed algorithm utilizes a data structure more efficient than previous ones, which takes the form of an indexed list. It also incorporates efficient realigning and mining techniques for handling incremental data and accurately mining results. Experimental results show the superiority of the proposed approach in terms of runtime, memory usage, scalability, and accuracy.
A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection

Authors:
raw_author_name: Jiahao Yu
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
raw_author_name: Xin Gao
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
raw_author_name: Baofeng Li
raw_affiliation_string: China Electric Power Research Institute Company Limited, Beijing, 100192, China
raw_author_name: Feng Zhai
raw_affiliation_string: China Electric Power Research Institute Company Limited, Beijing, 100192, China; School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
raw_author_name: Bing Xue
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
raw_author_name: Shiyuan Fu
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
raw_author_name: Chun Xiao
raw_affiliation_string: State Grid Shanxi Marketing Service Center, Taiyuan, 030032, China

Abstract: While existing reconstruction-based multivariate time series (MTS) anomaly detection methods demonstrate advanced performance on many challenging real-world datasets, they generally assume the data only consists of normal samples when training models. However, real-world MTS data may contain significant noise and even be contaminated by anomalies. As a result, most existing approaches easily capture the pattern of the contaminated data, making identifying anomalies more difficult. Although a few studies have aimed to mitigate the interference of the noise and anomalies by introducing various regularizations, they still employ the objective of fully reconstructing the input data, impeding the model from learning an accurate profile of the MTS's normal pattern. Moreover, it is difficult for existing methods to apply the most appropriate normalization schemes for each dataset in various complex scenarios, particularly for mixed-feature MTS. This paper proposes a filter-augmented auto-encoder with learnable normalization (NormFAAE) for robust MTS anomaly detection. Firstly, NormFAAE designs a deep hybrid normalization module. It is trained with the backbone end-to-end in the current training task to perform the optimal normalization scheme. Meanwhile, it integrates two learnable normalization sub-modules to deal with the mixed-feature MTS effectively. Secondly, NormFAAE proposes a filter-augmented auto-encoder with a dual-phase task. It separates the noise and anomalies from the input data by a deep filter module, which facilitates the model to only reconstruct the normal data, achieving a more robust latent representation of MTS. Experimental results demonstrate that NormFAAE outperforms 17 typical baselines on five real-world industrial datasets from diverse fields.
An online bi-objective scheduling algorithm for service provisioning in cloud computing

Authors:
raw_author_name: Yuxiao Qi
raw_affiliation_string: School of Software, Shandong University, Jinan, China
raw_author_name: Li Pan
raw_affiliation_string: School of Software, Shandong University, Jinan, China
raw_author_name: Shijun Liu
raw_affiliation_string: School of Software, Shandong University, Jinan, China

Abstract: To reduce costs and risks, service providers can purchase Infrastructure-as-a-Service (IaaS) instances from public clouds to deploy service applications and deliver job-execution services to users. However, it is challenging to make optimal instance purchasing and job scheduling decisions due to diverse instance billing methods and random service demands. In order to minimize time average service costs while ensuring quality of service (QoS), we consider that service providers purchase combinations of reserved and on-demand instances to meet the workload of their service system. Service providers need to dynamically determine the amount of on-demand and reserved instances to purchase for job executions, and then schedule jobs to run at the appropriate time. In this paper, we first propose a method to dynamically purchase reserved instances based on the historical usage data of on-demand and reserved instances. Then, we use a variable-length cycle scheduling mode for the job processing system and propose an online bi-objective scheduling algorithm based on Lyapunov optimization to schedule jobs. Even without the knowledge about the future, our online algorithm can dynamically purchase on-demand instances in an optimal way under the current number of reserved instances, and schedule jobs in varying time periods according to the arrival of jobs and optimization objectives. Based on both synthetic and real-world data, extensive experimental results show that our algorithm can achieve bi-objective optimization of minimizing time average cost while ensuring QoS.
DSRNet: Depth Super-Resolution Network guided by blurry depth and clear intensity edges

Authors:
raw_author_name: Hui Lan
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xian 710071, China
raw_author_name: Cheolkon Jung
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xian 710071, China

Abstract: Although high resolution (HR) depth images are required in many applications such as virtual reality and autonomous navigation, their resolution and quality generated by consumer depth cameras fall short of the requirements. Existing depth upsampling methods focus on extracting multiscale features of HR color image to guide low resolution (LR) depth upsampling, thus causing blurry and inaccurate edges in depth. In this paper, we propose a depth super-resolution (SR) network guided by blurry depth and clear intensity edges, called DSRNet. DSRNet differentiates effective edges from a number of HR edges with the guidance of blurry depth and clear intensity edges. First, we perform global residual estimation based on an encoder–decoder architecture to extract edge structure from HR color image for depth SR. Then, we distinguish effective edges from HR edges in the decoder side with the guidance of LR depth upsampling. To maintain edges for depth SR, we use intensity edge guidance that extracts clear intensity edges from HR image. Finally, we use residual loss to generate accurate high frequency (HF) residual and reconstruct HR depth maps. Experimental results show that DSRNet successfully reconstructs depth edges in SR results as well as outperforms the state-of-the-art methods in terms of visual quality and quantitative measurements.1
A Novel Fall Detection Framework With Age Estimation Based on Cloud-Fog Computing Architecture

Authors:
raw_author_name: Deyu Lin
raw_affiliation_string: School of Software, Nanchang University, Nanchang, China
raw_author_name: Weidong Min
raw_affiliation_string: School of Mathematics and Computer Science, Nanchang University, Nanchang, China
raw_author_name: Qing Han
raw_affiliation_string: School of Mathematics and Computer Science, Nanchang University, Nanchang, China
raw_author_name: Kaifei He
raw_affiliation_string: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China
raw_author_name: Ziyuan Yang
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu, China
raw_author_name: Xin Lei
raw_affiliation_string: School of Software, Nanchang University, Nanchang, China
raw_author_name: Bin Guo
raw_affiliation_string: School of Software, Nanchang University, Nanchang, China

Abstract: Falls are one of the main causes of injuries and even fatalities among the elderly. However, few existing research on fall detection focuses on the supervision of the elderly. Furthermore, the face image of the target usually exhibits low resolution in the surveillance videos, posing a challenge in achieving a balance between the response latency and the computational cost. To this end, a novel fall detection framework with age estimation based on cloud-fog computing architecture is proposed in this paper. Specifically, an optimized Soft Stage Regression-Shallow (SSR-S) network is presented to achieve excellent performance on age estimation for the low-resolution images collected by the edge layer. A fall pre-judgment mechanism and a Shallow-Convolutional Neural Networks (S-CNN) are proposed to make a better judgment on fall behaviors among different age groups at the fog layer and the cloud layer respectively. Besides, an age estimation-based priority algorithm is presented to prioritize the people aged 60 or older in fall detection at the cloud layer, with the aim to make a trade-off between the response latency and the computational overhead. Finally, extensive simulations have been conducted to evaluate the performance of our proposal. Experimental results have shown that the minimum Mean Absolute Error (MAE) of SSR-S reaches 7.59. The fall prejudgment mechanism can achieve 0% miss rate, and the accuracy of S-CNN reaches 90.5%. The detection speed of the overall framework is 17.0 frames per second (FPS).
Taking 5 minutes protects you for 5 months: Evaluating an anti-phishing awareness video

Authors:
raw_author_name: Benjamin M. Berens
raw_affiliation_string: SECUSO, Karlsruhe Institute of Technology, Kaiserstraße 12, Karlsruhe, 76131, Baden-Württemberg, Germany
raw_author_name: Mattia Mossano
raw_affiliation_string: SECUSO, Karlsruhe Institute of Technology, Kaiserstraße 12, Karlsruhe, 76131, Baden-Württemberg, Germany
raw_author_name: Melanie Volkamer
raw_affiliation_string: SECUSO, Karlsruhe Institute of Technology, Kaiserstraße 12, Karlsruhe, 76131, Baden-Württemberg, Germany

Abstract: Phishing is one of the biggest security threats to organizations. Anti-phishing awareness measures can improve phishing email detection rates. These measures need to be efficient, effective, and have an enduring impact over months, rather than days. Related research provides evidence of their effectiveness in the short term. However, questions remain as to how long this impact endures. We conducted a retention user study in two phases, with almost 200 participants in the first phase and almost 80 in the second phase, to determine whether a five-minute video retains its effectiveness five months after the intervention (similar to related work on more time-intensive measures). Our results suggest that short videos can indeed still exert a positive influence five months later. We also report on the video's influence on phishing detection strategies, as well as on viewers' confidence in this respect. Based on our results, we propose recommendations to inform the content of future awareness refreshment measures.