Reviewerly/exHarmony huggingface dataset:
An Adversarial Time–Frequency Reconstruction Network for Unsupervised Anomaly Detection

Detecting anomalies in massive volumes of multivariate time series data, particularly in the IoT domain, is critical for maintaining stable systems. Existing anomaly detection models based on reconstruction techniques face challenges in distinguishing normal and abnormal samples from unlabeled data, leading to performance degradation. Moreover, accurately reconstructing abnormal values and pinpointing anomalies remains a limitation. To address these issues, we introduce the Adversarial Time-Frequency Reconstruction Network for Unsupervised Anomaly Detection (ATF-UAD). ATF-UAD consists of a time reconstructor, a frequency reconstructor and a dual-view adversarial learning mechanism. The time reconstructor utilizes a parity sampling mechanism to weaken the dependency between neighboring points. Then attention mechanisms and graph convolutional networks (GCNs) are used to update the feature information for each point, which combines points with close feature relationships and dilutes the influence of abnormal points on normal points. The frequency reconstructor transforms the input sequence into the frequency domain using a Fourier transform and extracts the relationship between frequencies to reconstruct anomalous frequency bands. The dual-view adversarial learning mechanism aims to maximize the normal values in the reconstructed sequences and highlight anomalies and aid in their localization within the data. Through dual-view adversarial learning, ATF-UAD minimizes reconstructed value errors and maximizes the identification of residual outliers. We conducted extensive experiments on nine datasets from different domains, and ATF-UAD showed an average improvement of 6.94% in terms of F1 score compared to the state-of-the-art method.
A portfolio selection of internet of things (IoTs) applications for the sustainable urban transportation: A novel hybrid multi criteria decision making approach

The nature of technological innovations is unpredictable, therefore decision making on disruptive technologies such as the Internet of Things (IoT) in many public sector organizations is undisciplined. Due to resource limitations and tremendous payoffs, there is a need to have a decision-making framework to manage technological innovations as a portfolio of investments that balances risk and reward simultaneously. This study provides a new portfolio matrix for decision making to identify IoT applications in the urban transportation sector for future investment based on two dimensions of impact on sustainable development (SD) and feasibility for implementing IoT using a novel hybrid multi criteria decision making approach. For this purpose, seventeen IoT applications in urban transportation were identified using a systematic literature review. The improved Fuzzy Cognitive Map (FCM) - Best Worth Method (BWM) method was used to determine the relationship between SD criteria and IoT challenges, and to calculate their importance. Then, the Additive Ratio Assessment (ARAS) method was used to prioritize the IoT applications based on SD criteria and IoT challenges. Finally, the adapted impact-feasibility matrix was used to simultaneously evaluate both criteria for selecting the IoT applications portfolio. Results of this paper show that bike sharing and car sharing have the first priority for investment in the case of a developing country.
Compiler Optimization for Quantum Computing Using Reinforcement Learning

Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the development of compilers for quantum computing is still in its infancy—lacking mutual consolidation on the best sequence of passes, compatibility, adaptability, and flexibility. In this work, we take advantage of decades of classical compiler optimization and propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. Through distinct constraints and a unifying interface, the framework supports the combination of techniques from different compilers and optimization tools in a single compilation flow. Experimental evaluations show that the proposed framework—set up with a selection of compilation passes from IBM’s Qiskit and Quantinuum’s TKET—significantly outperforms both individual compilers in 73% of cases regarding the expected fidelity. The framework is available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).
BP-NTT: Fast and Compact in-SRAM Number Theoretic Transform with Bit-Parallel Modular Multiplication

Number Theoretic Transform (NTT) is an essential mathematical tool for computing polynomial multiplication in promising lattice-based cryptography. However, costly division operations and complex data dependencies make efficient and flexible hardware design to be challenging, especially on resource-constrained edge devices. Existing approaches either focus on only limited parameter settings or impose substantial hardware overhead. In this paper, we introduce a hardware-algorithm methodology to efficiently accelerate NTT in various settings using in-cache computing. By leveraging an optimized bit-parallel modular multiplication and introducing costless shift operations, our proposed solution provides up to 29× higher throughput-per-area and 10-138× better throughput-per-power compared to the state-of-the-art.
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs

Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e.g., computation, I/O, and memory-bound) edge computing systems. While efficient execution of their computational graph requires an effective scheduling algorithm, generating the optimal scheduling solution is a challenging NP-hard problem. Furthermore, the complexity of scheduling DNN computational graphs will further increase on pipelined multi-core systems considering memory communication cost, as well as the increasing size of DNNs. Using the synthetic graph for the training dataset, this work presents a reinforcement learning (RL) based scheduling framework RESPECT, which learns the behaviors of optimal optimization algorithms and generates near-optimal scheduling results with short solving runtime overhead. Our framework has demonstrated up to ∼ 2.5 × real-world on-chip inference runtime speedups over the commercial compiler with ten popular ImageNet models deployed on the physical Coral Edge TPUs system. Moreover, compared to the exact optimization methods, the proposed RL scheduling improves the scheduling optimization runtime by up to 683× speedups compared to the commercial compiler and matches the exact optimal solutions with up to 930× speedups. Finally, we perform a comprehensive generalizability test, which demonstrates RESPECT successfully imitates optimal solving behaviors from small synthetic graphs to large real-world DNNs computational graphs.
MES-Attacks: Software-Controlled Covert Channels based on Mutual Exclusion and Synchronization

Multi-process concurrency is effective in improving program efficiency and maximizing CPU utilization. The correct execution of concurrency is ensured by the mutual exclusion and synchronization mechanism (MESM) that manages the shared hardware and software resources. We propose MES-Attacks, a new set of software-controlled covert channel attacks based on MESM to transmit confidential information. MES-Attacks offer several advantages: 1) the covert channels are constructed at software level and can be deployed on any hardware; 2) the closed share of resource ensures the quality of the channels with low interference and makes them hard to be detected; and 3) the attack utilizes system’s software resources which are abound and hence difficult to isolate. We report the covert channels we have built with the following MESMs on Linux and Windows: flock, FileLockEX, Mutex, Semaphore, Event and WaitableTimer. Experimental results demonstrate that these covert channels can achieve transmission rate of 13.105 kb/s, 12.383 kb/s, and 6.552 kb/s, respectively in the scenarios of local, cross-sandbox and cross-virtual machine, all with bit error rate under 1%.
Adjusting counting-based secret-sharing via personalized passwords and email-authentic reliability

Counting-based secret-sharing (CBSS) is a current cybersecurity technique that provide applicable authentication for multi-participants collaborative trust access control utilization. The CBSS originally assigns its main confidential target key as secret random automatically selected to generate all participants private shares fulfilling trap-door function. CBSS is normally not allowing to entertain user preference in personalizing the passwords causing severe remembrance reliability difficulty. This research addressed this retention personalized challenge by adjusting the CBSS procedure commencing its process by user password selection choice, as for secret shares extraction to generate the target-key, securing the cybersecurity collaborative system. We proposed several algorithms for user-preference secret shares combination based on text and images alternatives, further verified by personal email convenience authentication. The proposals tested this adjusted CBSS reliability comparing overall security, systems number of shares possibilities, and variations functional time complexity as well as PSNR comparing to others for proper validation, noting its novel relevance attraction. The analysis remarked very interesting contribution comparison commented highest randomization secrecy of 95% for proposed model A passwords followed by 39%, 21%, 19% and 5%, for other different images-based passwords models B, C, D, and E, respectively. The work remarked attractive contribution compared to other works providing applicable CBSS real-life current utilization, opening door for promising progressive advancements to come.
DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.
Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers

Abstract This study investigated the factors influencing science teachers' 'Artificial Intelligence' (AI) utilization by using the 'Technology Acceptance Model' (TAM). The factors investigated alongside TAM variables were teachers' data like; age, sex, and residence type. TAM items that were correlated in this study included; self-esteem, stress and anxiousness, ease of utilization, behavioural intention, attitude towards AI usage, and expected benefits. The population of this study comprised all science teachers (170) in the Calabar Education Zone of Cross River State, Nigeria. The sample was made up of 79 science teachers comprising (58.22%) 46 females and (41.77%) 33 males. The descriptive and analytical research design was used in this study. A questionnaire named ' Approval of Artificial Intelligence: The Teachers' Eye' Questionnaire (AAITEQ) was used for the study. This study raised 3 research questions. The reliability for AAITEQ was from 0.72 to 0.81 using Cronbach's alpha. Findings indicated that the approval for the utilization of AI was high with an overall mean score of 3.00. The highest predicting value for behaviour intent using TAM variables was the ease of usage r = .789. Science teachers' sex (t, 77 = 1.988; p = .060, ( p ˃ .05), age F (2, 76) = .547; p = .581 ( p ˃ .05) and teachers' residence location (t, .77 = .533; p = − .062 ( p ˃ .05) did not influence the behaviour of science teachers' intention of the utilization of AI. It was recommended that both in-service and pre-service teachers be trained on the utilization of AI.
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey

Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the 21st century. However, in many applications, users require an explanation of the Artificial Intelligences decision. This led to increased demand for Comprehensible Artificial Intelligence. Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way. This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. Furthermore, we contribute by arguing that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. By introducing the parent concept Comprehensible Artificial Intelligence, we provide a clear-cut distinction of both concepts while accounting for their similarities. Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs. In addition, a comprehensive overview of the research on Comprehensible Artificial Intelligence on Knowledge Graphs is presented and put into the context of the taxonomy. Finally, research gaps in the field of Comprehensible Artificial Intelligence on Knowledge Graphs are identified for future research.
Bird Object Detection: Dataset Construction, Model Performance Evaluation, and Model Lightweighting

The application of object detection technology has a positive auxiliary role in advancing the intelligence of bird recognition and enhancing the convenience of bird field surveys. However, challenges arise due to the absence of dedicated bird datasets and evaluation benchmarks. To address this, we have not only constructed the largest known bird object detection dataset, but also compared the performances of eight mainstream detection models on bird object detection tasks and proposed feasible approaches for model lightweighting in bird object detection. Our constructed bird detection dataset of GBDD1433-2023, includes 1433 globally common bird species and 148,000 manually annotated bird images. Based on this dataset, two-stage detection models like Faster R-CNN and Cascade R-CNN demonstrated superior performances, achieving a Mean Average Precision (mAP) of 73.7% compared to one-stage models. In addition, compared to one-stage object detection models, two-stage object detection models have a stronger robustness to variations in foreground image scaling and background interference in bird images. On bird counting tasks, the accuracy ranged between 60.8% to 77.2% for up to five birds in an image, but this decreased sharply beyond that count, suggesting limitations of object detection models in multi-bird counting tasks. Finally, we proposed an adaptive localization distillation method for one-stage lightweight object detection models that are suitable for offline deployment, which improved the performance of the relevant models. Overall, our work furnishes an enriched dataset and practice guidelines for selecting suitable bird detection models.
An efficient image-guided-based 3D point cloud moving object segmentation with transformer-attention in autonomous driving

For intelligent transportation systems, moving object segmentation (MOS) provides valuable information for robots and intelligent vehicles, such as collision avoidance, path planning, and static map construction. However, all existing 3D point cloud MOS methods are based on LiDAR-only, which limits the ability to fuse supplementary information from different sensors. In this article, we solve the robust and accurate 3D MOS problem by designing a dual-stream network that integrates point clouds and images. We propose a perspective residual mechanism to mine the spatio-temporal motion information of point clouds, and design a fusion module based on Transformer Attention to extract multi-scale feature information from point clouds and images, improving the segmentation integrity of moving objects. Many experiments on the benchmark dataset show the superiority of our method. On the Semantic-KITTI, we outperform the advanced method by 6.5% mIoU. And we further apply our proposed model to the Semantic-KITTI: Moving Object Segmentation competition and achieve an advanced ranking on the leaderboard.
Real-Time Video Super-Resolution with Spatio-Temporal Modeling and Redundancy-Aware Inference

Video super-resolution aims to generate high-resolution frames from low-resolution counterparts. It can be regarded as a specialized application of image super-resolution, serving various purposes, such as video display and surveillance. This paper proposes a novel method for real-time video super-resolution. It effectively exploits spatial information by utilizing the capabilities of an image super-resolution model and leverages the temporal information inherent in videos. Specifically, the method incorporates a pre-trained image super-resolution network as its foundational framework, allowing it to leverage existing expertise for super-resolution. A fast temporal information aggregation module is presented to further aggregate temporal cues across frames. By using deformable convolution to align features of neighboring frames, this module takes advantage of inter-frame dependency. In addition, it employs a hierarchical fast spatial offset feature extraction and a channel attention-based temporal fusion. A redundancy-aware inference algorithm is developed to reduce computational redundancy by reusing intermediate features, achieving real-time inferring speed. Extensive experiments on several benchmarks demonstrate that the proposed method can reconstruct satisfactory results with strong quantitative performance and visual qualities. The real-time inferring ability makes it suitable for real-world deployment.
The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework

Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial industry is rapidly embracing AI due to its potential for high-cost savings in financial services. AI could transform the financial sector by creating opportunities for tailored, faster, and more cost-effective services. Saudi Arabia is emerging as a fast-growing market in this industry with a strong commitment to technology-driven institutions. While AI is gaining prominence and receiving government support, it has not yet become a critical component for enhancing the efficiency of financial transactions. Limited published research on AI adoption in the Saudi Arabian financial industry calls for a comprehensive literature review to examine the current state of AI implementation in this sector. Therefore, this study explores the benefits, limitations, and challenges of leveraging AI in finance, highlighting the importance of ethical and regulatory considerations for successful AI adoption in the sector. This study’s findings reveal that research has been conducted on how AI improves processes in the financial sector by integrating critical components and efficient algorithms tailored to the industry’s needs. Based on these findings, this study proposes a sequential framework at the macro and micro levels of management to guide AI’s development and integration into the financial sector. Additionally, the framework draws insights from the existing literature to provide a detailed understanding of opportunities, challenges, and areas for improvement to maximize AI’s potential in the Saudi Arabian financial sector.
A human-like action learning process: Progressive pose generation for motion prediction

Human motion prediction is crucial for the human–robot inter-action and self-driving. We human beings learn an action with two stages, i.e., the approximation stage and the adjustment stage. While the first stage learns an approximate pose in general, the second stage learns more details. Based on this observation, this paper proposes a new two-stage framework to predict the upcoming human motion poses progressively based on a historical sequence. In the approximation stage, we adopt the motion attention network to derive an approximation of the upcoming poses. In the adjustment stage, we propose a novel fine-tuning network that can extract both spatial and temporal features to enhance the prediction accuracy. To imitate the action learning procedure of our humans, the adjustment stage in our progressive prediction framework continuously adjusts the pose many times via a series of fine-tuning networks. Extensive experiments on three benchmark datasets (i.e., Human3.6M, CMU-Mocap, and 3DPW) show that our proposed method can outperform state-of-the-art methods in both short-term and long-term predictions.