Reviewerly/exHarmony huggingface dataset:
Augmented AI-Knowledge Driven Intelligent Systems for Adversarial-Dynamic Uncertainty and Complexity

Goal: ISO 31000 Risk Management (RM) recently re-defined risk as the effect of uncertainty on an organization's ability to meet the objectives. Earlier, it defined risk as a combination of the probability and scope of the (predicted) consequences. The revised ISO Risk advances beyond a static world guided by prediction and pre-determination based on historical data to a dynamic world characterized by uncertainty and complexity focused on business outcomes over data inputs. Our Knowledge Management (KM) R&D adopted by global organizations such as Nasa and Big Banks is readily applicable to provide a 25-year head start to organizations in above ISO risk evolution. Results: Over the last two decades, we have developed theoretical and applied frameworks for the dynamic world characterized by uncertainty and complexity, with business outcomes as drivers of real-time performance rather than data inputs. Our forward-looking anticipation of surprise focus of KM drives future organizational adaptation, survival and competence in face of discontinuous environmental change at organizations such as Goldman Sachs. Our KM focus manages change, uncertainty and complexity as primary (outcome) targets in contrast to data-driven (input) approaches. Its focus on dynamic uncertainty is complemented by adversarial uncertainty from cyber-adversarial environments. Originality | Value: Quantum uncertainty – encapsulating the two uncertainty types – and time-space complexity from increasingly non-deterministic and statistically non-normal and non-linear environments are the focus of our KM R&D underpinning development of quantum minds. Our latest AI-Cybersecurity KM practices are advancing the future of Pentagon’s C4I-Cyber-Command-Control-Advanced Battle Management Systems and AWS Network-Centric Agile-Resilient Cloud Computing.
Optimal Efficiency Assembly of UAV Formation Based on Improved Bald Eagle Search Algorithm

Although the Dubins path planning method is widely used in UAV flight path planning, the existing Dubins path planning usually sets the minimum turning radius to a certain value, ignoring the influence of UAV flight speed on the minimum turning radius. In addition, most existing path planning only considers the space coordination of UAVs, but fails to fully consider the time coordination of UAVs, namely the simultaneous arrival problem. Therefore, this paper studies the optimal efficiency aggregation of UAV formation based on improved Condor algorithm, aiming at the formation transformation of UAV cluster. Under the condition of time coordination, the effects of different flight speeds on Dubins’ path planning are considered, and the energy consumption and completion time of formation transformation are selected as efficiency indexes to give the optimal spatial location of formation assembly. The simulation results show that this model can achieve the desired optimal efficiency formation transformation.
Collaborative Working Spheres for Global Software Development Education During the COVID-19 Pandemic: An International Experience

This paper presents our experience implementing Collaborative Working Spheres (CWS) in the context of an international undergraduate course on Global Software Development (GSD) during the COVID-19 pandemic. Many universities around the world increased their efforts in creating educational alternatives for adequately addressing the educational challenges that this pandemic has introduced. A particular case is the training of software skilled graduates to work in globally distributed environments because learning this topic requires a lot of practical work when student motivation could have been negatively affected by the COVID-19 pandemic. However, providing highly practical GSD courses during a pandemic is a challenging task for many of these universities. It is against this backdrop that we have developed an educational tool to provide CWS, enabling undergraduate students to acquire practical experience in GSD and improve their communication and teamworking skills, even during the COVID-19 pandemic. An international empirical evaluation was conducted involving students and teachers from seven universities in different countries around the world. The obtained results showed that our approach can make a significant contribution to the development of practical projects on undergraduate GSD courses with students developing their knowledge and social skills associated with this topic. The data collected on the teachers’ perceptions suggested that our approach could also be useful in introducing the GSD approach at undergraduate level when social distancing is in place.
High-Fidelity Multiplexed Single-Shot Readout for Transmon Qubits With High-Power Measurement

Single-shot readout of a quantum state is an essential component in building a large-scale quantum processor, and reducing the readout error is the key challenge. To achieve a high signal-to-noise ratio, quantum-limited low-noise amplifiers such as Josephson parametric amplifiers are typically used. However, a high-fidelity single-shot readout method without a parametric amplifier would be advantageous, as it would reduce system complexity. In this paper, we present a high-fidelity result of frequency-multiplexed, high-power single-shot readout for superconducting qubits <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> using a parametric amplifier. We achieved a high readout fidelity of 98.3% with a 400 ns pulse and confirmed that the high-power readout can be used in tomography of an entangled state. We have demonstrated the potential usefulness of this technique in characterizing the final quantum state at the end of quantum operations in a superconducting quantum processor.
WEB-BASED SALES INFORMATION SYSTEM AT TOKO MANDIRI ELECTRONIC USING SOFTWARE DEVELOPMENT LIFE CYCLE (SDLC) METHOD

Toko Mandiri Elektronik is an electronics store in binjai that sells various types of electronics. Stores still use conventional methods (brochures, mouth to mouth). if there are additional items, the store must also manually change the item catalog to the store catalog. this research provides a solution for designing a website-based system that makes it easier for Toko Mandiri Elektronik in the product marketing process that can increase sales of electronic products at Toko Mandiri Elektronik. System development used by the author in analyzing the system using the SDLC method. The system generates a sales report in the form of a table which is useful for stores in searching data. The store catalog system is already in the form of a database, so the search process for product data will be faster and clearer.
The Method of Human-Machine Division for UAV System Based on Double OODA Loop

The division of work between man and machine is important to improve the autonomous capability of UAV system. In view of the problems of overload and out of loop in the traditional human-machine division methods, this paper proposes a human-machine division method of UAV system based on double OODA loop from the perspective of human-machine cooperation. Firstly, the principle of human-machine division of UAV system is expounded. The human-machine division thought of the double OODA loop is analyzed in detail. Then, the specific strategies of human-machine division under the three modes are put forward, which are man in the loop, man on the loop and man off the loop. The proposed method can effectively solve the autonomous control problem of autonomous control about the variable authority between the operator and UAV, which improves the coordination and cooperation ability between the operator and UAV. In addition, it can provide reference for the division of autonomous levels of UAV system.
Attention Augmented Face Morph Detection

Morph detection is of paramount significance when the integrity of Automatic Face Recognition (AFR) systems is concerned. Considering the risks incurred by morphing attacks, a robust automated morph detector is required which can distinguish authentic bona fide samples from altered morphed images. We leverage the wavelet sub-band decomposition of an input image, yielding the fine-grained spatial-frequency content of the input image. To enhance detection of morphed images, our goal is to find the most discriminative information across frequency channels and spatial domain. To this end, we propose an end-to-end attention-based deep morph detector which assimilates the most discriminative wavelet sub-bands of a given image which are obtained by a group sparsity representation learning scheme. Specifically, our group sparsity-constrained Deep Neural Network (DNN) learns the most discriminative wavelet sub-bands (channels) of an input image while the attention mechanism captures the most discriminative spatial regions of input images for the downstream task of morph detection. To this end, we adopt three attention mechanisms to diversify our refined features for morph detection. As the first attention mechanism, we employ the Convolutional Block Attention Module (CBAM) which provides us with refined feature maps. As the second attention mechanism, compatibility scores across spatial locations and output of our DNN highlights the most discriminative regions, and lastly, the multiheaded self-attention augmented convolutions account for our third attention mechanism. We evaluate the efficiency of our proposed framework through extensive experiments using multiple morph datasets that are compiled using bona fide images available in the FERET, FRLL, FRGC, and WVU Twin datasets. Most importantly, our proposed methodology has resulted in reduction in detection error rates when compared with state-of-the-art results. Finally, to further assess our multi-attentional morph detection, we delve into different combinations of attention mechanisms via a comprehensive ablation study.
A Practical Cooperative Coverage Path Planning Method of Multiple Fixed-Wing UAVs for an Aerial Imaging Mission

This paper investigates the cooperative coverage path planning problem of multiple fixed-wing UAVs. For an aerial imaging mission, the camera model mounted on the fixed-wing UAV pointing forward-down is established. Then, a cooperative back-and-forth (BF) flight pattern based on the relationship between the minimum turning radius and camera footprint is proposed. Compared with the traditional distributed collaborative approach, the presented cooperative method is proved to be superior in terms of the curve path length of turns, thus is more efficient and energy saving. Finally, one numerical simulation is put forward to validate the effectiveness of the proposed cooperative path planning method.
A Mini-UAV Lightweight Target Detection Model Based on SSD

Mini-UAV can not carry high-performance computing equipment, and the conventional neural network model is difficult to deploy to Mini-UAV because of its large scale and complex calculation. To solve the problem of the huge amount of computation of the deep learning model, we introduce a lightweight object detection network model for Mini-UAV that greatly reduces the amount of model computation and parameters on the premise of ensuring the detection accuracy. In this paper, SSD is used as the benchmark object detection model, depthwise separable convolution and grouped convolution are used as the basic lightweight means. A simplified grouped heterogeneous convolution structure is introduced and a spatial/channel hybrid attention mechanism is also introduced to achieve high-low layer feature fusion. Pascal VOC 2012 dataset is used for training and testing. We compared our algorithm with various lightweight target detection models from the perspective of model accuracy and model size. The experimental comparison results show that our model can improve detection accuracy with a lower computational cost.
An Adaptive Filter Algorithm for Passive Co-tracking

To enhance the adaptability of the passive co-tracking system in complex environment and improve the tracking accuracy, an adaptive filtering algorithm based on observation noise estimation is proposed. In the proposed framework, the Sage-Husa filtering technique with an improved noise estimator was adopted in UKF algorithm. The improved Sage-Husa algorithm utilized a dynamic statistic of the residual vectors to get better estimation accuracy and stability. In addition, a matching strategy based on a self-correlation portion of the covariance was implemented, which helped prevent the filtering divergence caused by the non-positive noise variance. The experimental results showed that the proposed algorithm can improve the tracking accuracy and robustness effectively.
Intra-Class Mixup for Out-of-Distribution Detection

Deep neural networks (DNNs) have found widespread adoption in solving image recognition and natural language processing tasks. However, they make confident mispredictions when presented with data that does not belong to the training distribution, i.e. out-of-distribution (OoD) samples. Research has shown that angular representations can be useful to address the curse of dimensionality and improve OoD detection performance. However, when evaluating the angular separability using Fisher’s criterion we find that empirical risk minimization and inter-class mixup trained DNNs have low angular separability between in-distribution data and OoD data. To improve angular separability, we propose intra-class mixup. We provide mathematical reasoning that shows that intra-class mixup results in reduced angular spread because of reduced variance at the input during training. Further, to take full advantage of improved angular separability from intra-class mixup we propose supplementing the separation metric with the cosine of angular margin to improve OoD detection. Angular margin is the angle between the final layer weight vector and the sample representation. The proposed intra-class mixup when applied to various existing OoD detection techniques shows an improvement of 4.21% and 6.21% in AUROC performance over empirical risk minimization and inter-class mixup, respectively. Further, intra-class mixup aided with the cosine of angular margin improves AUROC performance by 6.71% and 8.75% over empirical risk minimization and inter-class mixup, respectively.
Improving ANiTW Performance Using Bigrams Character Encoding and Identity-Based Signature

Several threats, such as misinformation and hoax, exist in social media as a frequently used technology to exchange information. These threats can be prevented if the reader can verify the message’s content integrity and source authenticity. One recent solution to overcome these threats was proposed by Ahvanooey et al. called ANiTW. ANiTW uses an invisible text watermarking scheme to verify social media message integrity and identify the message source. However, ANiTW is still vulnerable to undetected malicious text modification. Moreover, ANiTW has no tools to authenticate the extracted message source. The proposed method aims to overcome those vulnerabilities and improve ANiTW performance as a tool to verify message content integrity and message source authenticity in social media. To overcome vulnerabilities in ANITW, a part of the message hash and IBS-based signature is introduced as the watermark. The message integrity is verifiable by using part of the message hash as a watermark to detect any malicious. Moreover, implementing an IBS-based signature enables any reader to verify the message source’s authenticity easily. In addition, to improve ANITW performance in embedding capacity and invisibility, a novel bigram-based character encoding and embedding rule are also introduced. Based on the experiment and analysis result, it is proven that the proposed method can perform better than ANiTW in the security, embedding capacity, and invisibility aspects.
Generating Critical Nodes for Sub-path Planning with a Deep Neural Network Based on Resnet50

The Rapidly random-exploring tree* (RRT*) algorithm is widely used due to that it satisfies probabilistic completeness and asymptotic optimality, and don’t require the map construction. However, the convergence of RRT* is time consuming to find the optimal path. In order to solve this problem, we propose a two-stage path planning algorithm that first uses a deep neural network model for full path division and then performs the Informed RRT* planning algorithm for sub-path. The deep neural network model generates critical nodes on the optimal path to divide the full path, and the data set used for training is generated by the A* algorithm. The deep neural network model takes the form of dual input to adapt to the requirements of different clearance, and uses ResNet50 as the skeleton for extracting features. In the simulation experiments, compared with RRT* and Informed RRT*, the proposed algorithm reduces the time consumption by 47.1% and 26.3%, and shortens the planned path length by 4.1% and 4.3%, respectively.
An effective emotion tendency perception model in empathic dialogue

The effectiveness of open-domain dialogue systems depends heavily on emotion. In dialogue systems, previous models primarily detected emotions by looking for emotional words embedded in sentences. However, they did not precisely quantify the association of all words with emotions, which has led to a certain bias. To overcome this issue, we propose an emotion tendency perception model. The model uses an emotion encoder to accurately quantify the emotional tendencies of all words. Meanwhile, it uses a shared fusion decoder to equip the decoder with the sentiment and semantic capabilities of the encoder. We conducted extensive evaluations on Empathetic Dialogue. Experimental results demonstrate its efficacy. Compared with the state of the art, our approach has distinctive advantages.
Black-box simulation method: train the optical model from output

Accurate simulation is a critical requirement for modern optical systems, but precise theoretical modeling can be challenging due to various factors such as misalignment, theoretical approximations, and instrument errors. This paper presents the black-box simulation method, which addresses these limitations by training the optical system model using the optical field outputs. This approach leads to more accurate simulations, making it possible to effectively train optical Neural Network systems for improved performance.
Reflectance-based Code Optimization for Motion Deblurring

We propose a method for determining a code set based on the reflectance of the scene in the coded exposure photography technique using multiple color LEDs with same power. Coded exposure imaging affects image brightness, which affects motion deblurring performance. Thus, the performance of the motion deblurring depends on the duty cycle of the code. Previously, since the image was modulated with a single code, the duty cycle of the code considered only the overall brightness of the scene. The coded imaging with multiple color LEDs can consider the brightness of each color channel, allowing the code set to be further optimized based on the reflectance of each color in the scene. We demonstrated the effectiveness of our method by comparing it with conventional methods and confirmed that the average PSNR increases by 2.2dB.
Impacts of Clock Constraints on Side-Channel Leakage of HLS-designed AES Circuits

Many IoT devices such as FPGAs are at risk of side-channel attacks. To ensure security, cryptographic circuits such as AES must be implemented on FPGAs. In recent years, technologies to automatically generate RTL circuits from high-level languages such as C/C++ have become popular. In this paper, we design seven AES circuits by high-level synthesis and investigate the relationship between clock constraints and security. T-tests are used to evaluate the security from four metrics. Since the correlation varies depending on the metrics, the circuit design is realized by considering not only security but also circuit performance.
Improving Corruption Robustness with Random Erasing in the Frequency Domain

This study introduces a new data augmentation method to improve the corruption robustness of the convolutional neural network (CNN). Many data augmentation methods have been studied to reduce overfitting and to improve the generalization ability of CNNs. One of the most widely used data augmentation techniques is Random Erasing which erases a random rectangle region of an image. Most of the augmentation methods are applied in the spatial domain, but augmentation techniques in the frequency domain are less studied. In this study, the image is processed with 2D discrete Fourier transform (DFT), and then Random Erasing is applied in the frequency domain. Finally, the inversed image generated with inverse DFT is used as an input. As a result, the proposed method leads to the robustness improvement of the model against common corruptions.
A Survey on Causal Inference in Image Captioning

Although numerous research efforts in the field of Image Captioning (IC) have been conducted, the problem of dataset bias, which causes spurious correlations during training, remains. Recent studies in vision-language (VL) tasks have proposed causal inference as a debiasing method, showing significantly advanced performances. In this paper, we present a comprehensive survey of state-of-the-art IC models based on causal inference and categorize them according to their adjustment. We aim to provide an understanding of how causal inference can be utilized in IC. Furthermore, since IC is one of the foundations in VL tasks, we believe that this review will also help to get insight when applying causal inference in other VL tasks.
Korean Tokenization for Beam Search Rescoring in Speech Recognition

The performance of automatic speech recognition (ASR) models can be greatly improved by proper beam-search decoding with an external language model (LM). There has been an increasing interest in Korean speech recognition, but not many studies have been focused on the decoding procedure. In this paper, we propose a Korean tokenization method for neural network-based LM used for Korean ASR. Although the common approach is to use the same tokenization method for external LM as the ASR model, we show that it may not be the best choice for Korean. We propose a new tokenization method that inserts a special token, SkipTC, when there is no trailing consonant in a Korean syllable. Our experiments show that the proposed approach achieves a lower word error rate compared to the same LM model without SkipTC. In addition, we are the first to report the ASR performance for the recently introduced large-scale 7,600h Korean speech dataset.