Reviewerly/exHarmony huggingface dataset:
Link 16 Message Processing Based on Transaction

Data link is a key factor for achieving interoperability of combat units to enhance operational capability in modern net-centric operations. Aiming at improving the interoperability between combat units, we propose a general message processing mechanism and construct a transaction based model. Taking a typical function of link 16 for example, the proposed method of message processing is illustrated. Experiment results demonstrate that the transaction based method is able to realize standardization of message processing. Besides, the reachability, coverability and completeness of processing are verified via the executable Petri net model analysis.
Volodymyr Doroshenko about the development of the Shevchenko Scien¬tific Society Library under Soviet rule: illusions and realities (1940–1941)

The library of the Shevchenko Scientific Society in Lviv was founded in 1894 by the Shevchenko Scientific Society. This is the first and for a long¬time the only scientific collection of Ukrainian studies in Ukraine. Its leaders I. Krevetsky and V. Doroshenko were prominent librarians and bibliographers. The library collected all the most important Ukrainian and foreign publica¬tions about Ukraine, conducted an intensive international book exchange. In 1939, the library had 72,828 titles of books in 207,923 volumes and issues, 1,485 manuscripts and 2,449 maps. In addition, there were about 50,000 doub¬lets and tens of thousands of unlisted books. According to I. Franko’s will, his library and archive, which numbered 6,000 volumes and 500 manuscripts, were given to the SSS Library as a special value. After the accession of Western Ukraine to the Soviet Union in February 1940, it became part of the Lviv branch of the library of the USSR Academy of Sciences, which was subordinated to the Library of the Academy of Science in Kyiv. The director of the library, Volodymyr Doroshenko, hoped that the new government and Kyiv leadership would understand the importance of this unique Ukrainian book collection. The article highlights the desire of V. Doroshenko to preserve the integrity of the institution in the new conditions and its national character. The director’s efforts to enrich the composition of library book collections are reflected. The reasons and circumstances of the structural changes that the library underwent when it became a department of Ukrainian literature and language are highlighted. On the basis of archival sources, the striking losses of the institution’s funds as a result of planned systematic purges by the Bolshevik censorship bodies and the distribution of the SSS Library funds to the newly created departments of the Lviv branch of the Academy of Sciences are shown. Thus, on the example of this book collection the results of the Soviet library policy aimed at destroying the cultural heritage of the Uk¬rainian people are shown. Keywords: Volodymyr Doroshenko, Shevchenko Scientific Society Library in Lviv, Lviv branch of the library of the Academy of Sciences of the USSR, library censorship, Soviet library politics.
A Full-Reference Image Quality Assessment Method with Saliency and Error Feature Fusion

Image quality assessment (IQA) has obtained certain achievements with the help of convolutional neural network (CNN). To promote the evaluation performance, most existing methods focus on optimizing the structure and parameters of neural networks, while some useful features of image are ignored that can easily be acquired. In this paper, we propose a saliency and error feature fusion IQA (SEFF-IQA) method. Instead of the image itself, two image features, the error between the reference image and the distorted image, and the subjective saliency of distorted image are taken as inputs of the CNN for training. The evaluation score of image quality were obtained by a conventional CNN that trained on frequently used public databases. The proposed method possesses one basic architecture of the CNN only and reduces the volume of training data remarkably compared with state-of-art approaches. Experimental results show that the proposed method is more consistent with human subjective perception than other existing deep learning-based methods.
NeuroDFD: Towards Efficient Driver Face Detection with Neuromorphic Vision Sensor

Face detection is a fundamental task for various computer vision applications. With the rapid improvement of computing power, most face detection algorithms adopt complex deep neural net architectures to increase accuracy. However, such algorithms are difficult to be applied on the mobile platform with restrained computing resources. To this end, this paper introduces a novel neuromorphic vision based single-shot driver face detection method, named NeuroDFD to handle driver face detection problem. Different from traditional CMOS camera, the neuromorphic vision sensor captures pixel level brightness changes signal and output them asynchronously in the form of events. It has the characteristics of high dynamic range and low data redundancy, which helps to meet the challenges of in-cabin lighting conditions and limited computing resources of edge devices. Based on the unique output, we first construct event representation by discretizing the time domain and present light weight translation-invariant backbone to extract multi-scale features. Then, we propose shift FPN and shift context module to promote the spatial-temporal features extraction with limited computation cost. Extensive experiments prove that the NeuroDFD can achieve remarkable detection performance with high efficiency.
Development of an Improved Occluded Person Re-Identification System Using Deep Learning

One of the core challenges faced in developing a person Re-Identification (re-ID) model for open-set is solving the problem of intentional or unintentional occlusion of the subject or person of interest. There has been numerous research conducted on closed-set re-ID where the basic assumption is that the gallery set will only have non-occluded person images. This is not practical for real-world scenarios where the variables vary unpredictably. This paper examines the problem from an object detection perspective as well as surveys the state-of-the-art of person re-ID, their achievement and shortcomings. This paper has also brought into light the recent advancement in object detection algorithms outside person re-ID and how those techniques and ideas can help within the realm of the person of interest search. The aim is to realise a person re-ID system that is geared towards open-set Re-ID and can deal with person occlusion in a robust manner.
Convexifying State-Constrained Optimal Control Problems

This paper presents a method that convexifies state-constrained optimal control problems in the control-input space. The proposed method enables convex programming methods to find the globally optimal solution even if costs and control constraints are non-convex in control and convex in state, dynamics is non-affine in control and convex in state, and state constraints are convex in state. Under the above conditions, generic methods do not guarantee to find optimal solutions, but the proposed method does. The proposed approach is demonstrated in a sixteen-dimensional navigation example.
Research on Image Classification Improvement Based on Convolutional Neural Networks with Mixed Training

Convolutional neural networks (CNNs) have been widely used and have shown excellent predictive power in image classification. In this paper, an ensemble method, namely the Mixture of trained CNNs (M-tCNNs) method, was proposed to improve the classification performance evaluated on the CIFAR-100 dataset. The M-tCNNs consists of two components, including three expert networks (CNNs) and a trainable gating network. The classification performance of M-tCNNs method was compared to that of single CNN and simple average (SA) method. The results showed that the M-tCNNs method achieved a better accuracy (ACC) of 84.18% compared to single CNN (highest ACC = 78.74%) or SA method (ACC = 80.78%). Experimental results indicate that M-tCNNs method can improve the classification performance for CIFAR-100 dataset compared to single CNN and SA methods.
Indonesian Seismic Mitigation using Earthquake Predicted Artificial Intelligence Model

One way to reduce injuries during an earthquake is to determine the probability of an earthquake as early as possible so that rescue mitigation may be carried out. An artificial intelligence-based prediction model using a random forest regression algorithm was developed using a dataset of earthquake records in Indonesia from January 1900 to January 2022. The attributes used were date, location, latitude, longitude, hypocenter, magnitude, depth of the earthquake, maximum peak ground acceleration, modified Mercalli intensity, and site class. The label used to determine earthquake predictions was modified Mercalli intensity. The model was split into 80% training data and 20% testing data. The accuracy of the random forest regression model was 98.8%, and the error rate performance metrics mean absolute error and mean square error produce error values of 5.4% and 4.6%, respectively. This Artificial Intelligence-based earthquake prediction model is expected to provide input on the development of earthquake mitigation strategies in Indonesia. It is expected that there will be no more earthquake fatalities in the future.
Instance as Identity: A Generic Online Paradigm for Video Instance Segmentation

Modeling temporal information for both detection and tracking in a unified framework has been proved a promising solution to video instance segmentation (VIS). However, how to effectively incorporate the temporal information into an online model remains an open problem. In this work, we propose a new online VIS paradigm named Instance As Identity (IAI), which models temporal information for both detection and tracking in an efficient way. In detail, IAI employs a novel identification module to predict identification number for tracking instances explicitly. For passing temporal information cross frame, IAI utilizes an association module which combines current features and past embeddings. Notably, IAI can be integrated with different image models. We conduct extensive experiments on three VIS benchmarks. IAI outperforms all the online competitors on YouTube-VIS-2019 (ResNet-101 41.9 mAP) and YouTube-VIS-2021 (ResNet-50 37.7 mAP). Surprisingly, on the more challenging OVIS, IAI achieves SOTA performance (20.3 mAP). Code is available at https://github.com/zfonemore/IAI .
A novel algorithm for sarcasm detection using supervised machine learning approach

&lt;abstract&gt; &lt;p&gt;Sarcasm means the opposite of what you desire to express, particularly to insult a person. Sarcasm detection in social networks SNs such as Twitter is a significant task as it has assisted in studying tweets using NLP. Many existing study-related methods have always focused only on the content-based on features in sarcastic words, leaving out the lexical-based features and context-based features knowledge in isolation. This shows a loss of the semantics of terms in a sarcastic expression. This study proposes an improved model to detect sarcasm from SNs. We used three feature set engineering: context-based on features set, Sarcastic based on features, and lexical based on features. Two Novel Algorithms for an effective model to detect sarcasm are divided into two stages. The first used two algorithms one with preprocessing, and the second algorithm with feature sets. To deal with data from SNs. We applied various supervised machine learning (ML) such as k-nearest neighbor classifier (KNN), na?ve Bayes (NB), support vector machine (SVM), and Random Forest (RF) classifiers with TF-IDF feature extraction representation data. To model evaluation metrics, evaluate sarcasm detection model performance in precision, accuracy, recall, and F1 score by 100%. We achieved higher results in Lexical features with KNN 89.19 % accuracy campers to other classifiers. Combining two feature sets (Sarcastic and Lexical) has shown slight improvement with the same classifier KNN; we achieved 90.00% accuracy. When combining three feature sets (Sarcastic, Lexical, and context), the accuracy is shown slight improvement. Also, the same classifier we achieved is a 90.51% KNN classifier. We perform the model differently to see the effect of three feature sets through the experiment individual, combining two feature sets and gradually combining three feature sets. When combining all features set together, achieve the best accuracy with the KNN classifier.&lt;/p&gt; &lt;/abstract&gt;
Multimodal E-Commerce Product Classification Using Hierarchical Fusion

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems.
A New Quantitative Evaluation Strategy for Deep Generated Sar Images

Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) is the core application of SAR technology. The shortage of training data is a constraint for SAR-ATR. One of the valid methods to solve the problem is SAR image simulation. A large quantity of generative models have achieved impressive performances on SAR image simulation. Therefore, it is absolutely necessary to evaluate whether the simulated images fulfill the requirement of application. This challenging problem has not attracted plenty of attention. Very few studies have been done. To fill the blank, we propose a new evaluation strategy. Two quantitative measurements are proposed to evaluate the simulated images from the local and the global perspective respectively. The local measurement, Fréchet Inception Distance score (FID) is used to measure the distance of feature vector between the real images and the simulated images. Contrarily, the global measurement, Hybrid Recognition Rate curve (HRR) is developed to assess the application capability of the simulated images from a global perspective. Multiple comparative experiments are performed on real SAR dataset. The experimental results demonstrate the effectiveness of the proposed method.
Non-blocking Asynchronous Training for Reinforcement Learning in Real-World Environments

Deep Reinforcement Learning (DRL) faces challenges bridging the sim-to-real gap to enable real-world applications. In contrast to the simulated environments used in conventional DRL training, real-world systems are non-linear and evolve in an asynchronous fashion; sensors and actuators have limited precision; communication channels are noisy; and many components introduce variable delays. While these issues are known to many researchers, published methods for systematically tackling the problem of DRL training under these conditions without using simulation are sparse in the field. To this end, this paper proposes a non-blocking and asynchronous DRL training architecture for non-linear, real-time dynamical systems tailored to handling variable delays. Compared to conventional DRL training, we: (i) decouple the RL loop into separate processes run independently at their own frequencies, (ii) further decouple collection of transition tuples <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(s_{t}, at_{t}, s_{t+1})$</tex> via asynchronous and independent streaming of both actions and observations, and (iii) mitigate the effects of delays and increase sample efficiency by providing delay-length measurements to the training loop and regular retraining of the DRL network. This allows the action step time to be tuned to find an optimal control frequency for a given system, and handles streamed observations that arrive with random delays and independently of action timing. We demonstrate the efficacy of this architecture with a physical implementations of a commodity-grade swing-up pendulum and a quadrupedal robot. Our architecture achieves the best results balancing the pendulum for almost entire length of the episode, compared to conventional blocking approaches which fail to learn effective policies. Our results show that these techniques scale to more complex tasks such as quadrupedal locomotion.
Spatial-Channel Transformer for Scene Recognition

Despite the great success of attention mechanisms on object recognition, scene recognition remains a challenging problem. The reason is that discriminative regions are not evident in a scene image. For example, a tree in an image can be a cue to recognize a scene, but the tree cannot be the only cue for recognizing the scene. That means several scene categories (e.g. mountain, marsh, and river) can contain a tree. Thus sometimes, overall regions, rather than specific regions, need to be considered for scene recognition. To solve the problem, we propose Spatial-Channel Transformer (SC-Transformer). The SC-Transformer is a simple yet effective module that uses a new attention mechanism by incorporating the importance between the spatial and the channel domain for a given scene image. If the given scene image should be considered only within some specific regions, SC-Transformer turns off the channel attention, and vice versa. Furthermore, the attention mechanism used in our proposed method is advanced from previous approaches. Previous spatial and channel attention mechanisms were designed in a sequential or parallel manner. These mechanisms eventually combine spatial and channel attention together, so spatial and channel attention may often interfere with each other. In contrast to the previous works, we present a new mechanism that simultaneously considers spatial and channel attentions. We validate our approach on a large-scale scene recognition dataset and outperform the previous state-of-the-art spatial-channel attention mechanism. Experimental results demonstrate the efficacy of our attention mechanism for scene recognition.
Assessing the Effect of Interactivity Design In VR Based Second Language Learning Tool

Virtual Reality (VR), as a helpful tool in language education, is widely supported by the current literature. VR can provide a variety of stimulating scenarios that keep learner engagement high so its use in the language classroom has increased rapidly. This makes it necessary for further research to be conducted in the field to determine ways to maximize its potential. This research aims to determine if the level of interactivity presented in an Immersive VR Environment is a factor that will impact a user’s capability to successfully learn a second language, particularly when facing subjects with different age, gender, and previous VR experience. To satisfy these aims, 3 versions of a Virtual Reality Language Learning Application were created with varying levels of interactivity. Participants of this study were administered pre and post evaluations to analyze the efficiency of their second language learning experience. Our results show that the level of interactivity may not be a factor that impacts a user’s capability of learning and provide insight into the factors needed for successful language learning in Virtual Reality.
Multiagent Systems on Virtual Games: A Systematic Mapping Study

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> Games are a well-established scenario to test AI and multiagent systems (MAS) proposals due to their popularity and defiance. However, there is no big picture of the application of this technology to games, the evolution of the kind of problem tackled, or the game scenarios in which agents have been experimented. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> To perform a systematic mapping to characterize the state of the art in the field of MAS applied to virtual games and to identify trends, strengths, and gaps for further research. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> A systematic mapping study has been conducted to find primary studies in the field. A search was performed on title, abstracts, and keywords, whilst classification, data extraction, and further analysis were performed according to specific criteria focused on MAS papers with experimentation and evidence in a game scenario. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> 78 studies published between 1998 and 2021 were found. Studies have been classified according to the MAS problem faced and the agent reasoning strategy. We detect that machine learning is the most common AI technique for MAS in games, considering both reinforcement learning and evolutionary techniques. MAS are used in a variety of gaming genres, especially in real-time strategy (RTS), sports, and simulation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</i> RTS and sports games are well suited for concrete MAS problems, such as multiagent planning and task allocation. Expanding evidence and experimentation on other aspects related to scalability and usability issues is discussed. Those MAS problems and experiments that remain slightly modeled on games or are not thoroughly studied yet have been also identified.
Using Deep Learning to Detect Anomalies in Traffic Flow

Uncertainty is an ever present challenge in data analysis. In particular, it is important to detect, as precisely as possible, unforeseen phenomena. In this paper we study the usefulness of two deep learning based methods (CNN auto-encoder and BiLSTM auto-encoder) to detect anomalies in situations that can be defined in terms of time series. In order to evaluate our approaches, we consider traffic flow data and perform experiments in two orthogonal scenarios: a guided scenario (training only with data considered as 'normal' after a naïve labelling) and a basic scenario. Our results show that if we train the models using only the considered 'normal' data, the obtained models do not achieve good results because none of them are able to detect all type of abnormal data correctly. In contrast, both models can detect all type of time series anomalies when we consider the basic scenario.
Cryo-CMOS Model-Enabled 8-Bit Current Steering DAC Design for Quantum Computing

This paper reports an 8-bit current steering-type cryogenic digital-to-analog converter (DAC) module using HLMC 40nm low-power (40LP) technology for fast qubit initialization in quantum computers. Based on the generic design platform generated from our cryo-CMOS compact model, we optimize the circuit to ensure its correct function with temperature-insensitive non-linearity. Benefiting from the integration of the nA-range on-chip current reference and appropriate gate sizing, a low power consumption of 13.8 $\mu$W under the mixed power supply of l.1 V/1.8V is achieved in this DAC design with an active area of 0.0074 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . With the realization of the stable output range of 6 mV and fast sampling rate of 140 MS/s at low temperatures, our cryo-DAC is able to manipulate the physical qubit, hence laying a solid foundation for practical large-scale quantum computing applications.