Reviewerly/exHarmony huggingface dataset:
Novel Approaches to Calibration and Programming of MZI-Based Optical Processors: Overcoming Challenges and Enhancing Performance

In this presentation, we discuss challenges of calibrating and programming MZI-based programmable optical processors. We propose a novel architecture that tackles these issues, delivering faster and more energy-efficient programming while maintaining key performance metrics such as minimum insertion loss and resilience to fabrication imperfections.
Applications of Double Injection Photonic Devices

Double Injection in ring resonators revealed unique programmability properties in both, modulator and filter operation modes. We present here further applications of the device, namely its incorporation in photonic networks and multiple frequency handling.
Coherent Ising machine as a thermodynamic system

We experimentally observed a thermal phase transition of two-dimensional Ising model in a coherent Ising machine (CIM). The results indicate that the CIM well simulates the thermodynamic properties of the 2D Ising model that matches analytical and numerical solutions than the means field solutions.
Neuromorphic computing by means of recurrent spectrum slicing for next generation high baud rate transmission systems

In this paper we review our recent work in photonic neural networks based on recurrent optical spectrum slicing as potential accelerators for the mitigation of transmission impairments in short reach, high symbol rate (>100 Gbaud) transmission systems. Results will focus on self-coherent receiver architectures.
Neural Network Based Automatic Modulation Classification with Online Training

The use of wireless communication has been growing significantly. Given the limited radio spectrum available, it is important to use modulation techniques to squeeze the most information in the least amount of spectrum. To improve the throughput and reliability of communication, Automatic Modulation Classification (AMC) has become an essential topic in the research of Cognitive Radio (CR). AMC is a technique to identify the modulation scheme of the received radio signals without any transmission parameters. The state-of-the-art approach is to use a pre-trained classifier to determine the modulation in use. Unfortunately, all forms of distortions and interference are impossible to predict, and thus these approaches fail when new distortions or interference appear that were not previously learned. In this work, we developed a technique where the transmitter periodically sends out a known ordered sequence of signals of all the modulations to be used by the receiver. The receiver knows the transmission sequence and thus knows how to label these received signals containing each required modulation. We used an over-the-air (OTA) radio transmission between software-defined radio (SDR) devices, and the collected dictionary dataset was used to train a neural network at the receiver. Our results show that training the network in real-time while the radio is in use can enable significantly better performance than any of the existing approaches.
Stealthy Adversarial Attacks Against Automated Modulation Classification in Cognitive Radio

In cognitive radio systems, wireless spectrum sensing plays a crucial role in identifying the state of the wireless environment, which leads to the effective utilization of scarce spectral resources for various application fields. As a critical part of wireless spectrum sensing, automatic modulation classification (AMC) is used to identify the modulation types of the received signals automatically. With the advances in sensing and computing technologies, artificial intelligence (AI), especially deep learning (DL), has been widely applied to enhance the effectiveness and timely response of AMC. However, in recent years, increasing evidence shows that carefully-crafted adversarial noise can mislead learning models, which raises concerns about the trustworthiness of DL solutions in AMC. Therefore, it is crucial to sufficiently mitigate the adversarial perturbations in DL-powered AMC. To realize a successful mitigation strategy, it can be beneficial to first adopt an adversarial mindset and formulate threat models of practical perturbations in DL-powered AMC. However, there is still limited work focusing on generating practical adversarial attacks in DL-powered AMC that has complex domain knowledge. In this paper, we propose an innovative and stealthy adversarial attack method that can practically compromise the DL-based decision-making of AMCs. In the performance evaluation section, different scenarios will be considered to illustrate the effectiveness of our proposed adversarial attack method in achieving a high success rate and sufficient stealthiness in AMC.
Categories of Neural Networks

We introduce new categories of neural networks that we hope will help us expose and explore their underlying structure and relationships. Our categories represent neural networks as objects and present structure for comparing neural networks with the same number of layers. In this paper we discuss the construction, properties, and currently known limitations – all with specific, illustrative examples. There are a number of directions this work can move towards, and we conclude with a section on future work summarizing several of these directions.
Securing Space Cognitive Communication with Blockchain

Cognitive communication helps track active and inactive resident space objects, which is part of Space Situation Awareness (SSA). Gathering and analyzing SSA sensor data helps evaluate the space environment, which maintains and enhances Space Traffic Management (STM) coordination and accommodation. Securing cognitive communication is required to protect the accuracy of transmitted data and provide authorized entities access privileges to data and services. It is difficult to create a reliable centralized Identity authentication and access control (AC) system because of the decentralization and heterogeneity of space systems, which can either be a performance bottleneck or a single point of failure. Blockchain technology holds enormous potential for securing cognitive communication-based space networks. We introduce blockchain-enabled, capability-based access control, decentralized authentication, and an automatic vulnerability correction algorithm to help smart contract administrators patch problems and enable efficient protection for devices, services, and information in space networks. A proof-of-concept prototype will be developed and tested on an Ethereum version 2 which uses proof of stake (PoS) blockchain network using both resource-constrained devices (such as Raspberry Pi nodes simulating as satellites with sensor observations) and more powerful computing devices (such as laptops simulating as ground network). The experimentation will be evaluated with computation overhead, latency, anomaly detection contract, and processing overhead. The outcomes of the experiments will demonstrate our objective to provide a decentralized, scalable, lightweight, and fine-grained blockchain solution for securing space communication networks.
Wordle Results Distribution Prediction Model and Difficulty Classification Model Using Word Linguistic Properties

Wordle is a word guessing game offered daily by the New York Times. The aim of our study is to use selected word linguistic properties to predict the percentages of successfully guessing a word in one to six tries or failing and classify words by difficulty. A prediction model was established based on stepwise linear regression and normal distribution probability density function (PDF). The percentages of a word were predicted using stepwise linear regression. The Jarque Bera test using MATLAB function indicated that 97.5% of word attempt distributions followed a corresponding normal distribution. Thus, these seven percentages were used as independent variables to predict the mean and variance of the corresponding normal distribution. The PDF was then obtained to correct percentages. Additionally, a word classification model was also established based on K-Means and support vector machines (SVM) to classify words by difficulty. The elbow method was used to divide words into two categories. The classification model was trained based on SVM. The prediction results showed that the sum of predicted percentages for each word fell between 99 and 101, which was superior to performing a single stepwise linear regression. The average root mean square error per word was 3.22. The classification model results showed that, based on 5-fold cross validation, area under curve, accuracy, recall and precision were 0.83, 77.1%, 75.16% and 73.72%, respectively, indicating the reliability of our method.
A Kind of Paradigm-Based Software Cost Estimation Method Using LSTM-CRF

Software cost estimate is important not only to project plans but also to software budgeting. Although more and more modules and methods are presented, it seems that Artificial Intelligence is the only way to form relatively precise solutions. In this paper, we focus on Nesma, a kind of popular Function Point Measurement, and present 5 main paradigms to define heuristic rules to split the software into two layers, which are Pricing Objects and Measuring Objects, for precise mapping for ILF, EIF, EI, EQ, EO. Moreover, after large-scale information projects are set as training sets, we use paradigms to improve the performance of the original LSTM-CRF. It is illustrated that paradigms-based LSTM-CRF has achieved significant results in controlling the increase in accuracy and precision rate while improving performance. However, the recall rate remains high. Compared with experts' manual auditing results, the quantity and quality of the training samples and manually labeled texts still need to be improved.
A New Convolutional Neural Network Architecture for Mars Landmarks Classification Based on MobileNetV2

Convolutional neural networks have achieved outstanding state-of-the-art performance in many mainstream application scenarios. However, its application in niche but critical fields such as planetary science requires more exploration. Among those specific application scenarios, the Mars landmark classification is one of the ongoing tasks. While, due to issues with some in-use neural network architectures such as inadequate accuracy and excessively large size, this task has yet to be effectively fulfilled. To address these challenges, we proposed a new network architecture based on MobileNetV2 by introducing non-local operations and DropBlock. Also, with the help of label smoothing and transfer learning, the accuracy of our new proposed network architecture can achieve 93.64%, which outperforms several in-use models including VGG-16, original MobileNetV2, ResNet-34 and HiRISENet.
A Chinese Knowledge Graph Q&amp;A System Based on Dense Relationship Retrieval

Currently, most knowledge graph question answering (KGQA) systems need to retrieve all entities when dealing with complex problems involving multiple entities and relationships, which results in high time complexity and resource consumption. The response time of KGQA systems is an important indicator for evaluating their performance. To address this issue, this paper proposes a Chinese KGQA system based on dense relationship retrieval. The system uses the Faiss index mechanism for vector similarity retrieval, quickly extracts the top K relationships from the pre-built vector relationship library, and constructs paths to reduce a large number of irrelevant semantic paths in most KGQA tasks, thereby improving the time cost and computational resource overhead caused by the exponential growth of path numbers in second-order and higher-order questions. By using this method, the response time of the QA system can be shortened to within 1 second with minimal loss of accuracy. This paper combines pre-training models to complete tasks such as text semantic similarity and entity mention recognition, and achieves an average F1 value of 71.3% on the CCKS2019-CKBQA test set. Comparing with other systems demonstrates the superiority of our method in terms of accuracy and efficiency.
Impact of Deep Feature Synthesis on Deep Learning in Electronic Transaction Fraud Detection

Electronic transaction fraud has been a severe threat in recent years, causing substantial financial losses and devaluing the reputation of financial institutions. Various machine learning and deep learning models have been cited in the literature to detect electronic transaction fraud effectively. Feature selection is crucial in enhancing the performance of a machine learning model. Automatic feature engineering techniques outweigh the manual feature selection process regarding the time spent and adapting to the changing nature of the dataset. However, to the best of our knowledge, the impact of state-of-the-art feature selection techniques, such as Deep Feature Synthesis (DFS), on the performance of deep learning-based electronic transaction fraud detection has not been thoroughly studied. Our study uses the DFS algorithm to generate features automatically from the labelled credit card transaction dataset collected from open-source sites. The data is fed into a baseline deep learning model, i.e., Convolutional Neural Networks (CNN), in two separate cases- with DFS and without DFS. A comparison of results from both cases shows that CNN with DFS outperforms the standalone CNN significantly in terms of accuracy, precision, recall, and F1 scores.
Research in Blockchain-Based Manufacturing Supply Chain

This paper proposes an innovative approach for enhancing the efficiency and security of manufacturing supply chains by integrating the Internet of Things, blockchain technology, and genetic algorithm-based consensus mechanism. The IoT devices are employed to monitor and collect real-time data from various stages of the supply chain, ensuring transparency and traceability. The blockchain technology is utilized to store and secure the collected data, enabling immutable and decentralized data management. To achieve consensus in the Blockchain network, a GA-based consensus algorithm is introduced, which optimizes the routing and allocation of resources in the supply chain. The GA considers factors such as distance, capacity, and demand to minimize transportation costs and maximize resource utilization. Experimental results demonstrate that the proposed approach significantly improves the efficiency and reliability of the manufacturing supply chain, reducing operational costs and enhancing customer satisfaction. This research contributes to the development of intelligent and trustworthy supply chain systems by leveraging the synergies among IoT, blockchain, and GA-based consensus mechanisms.
Learning Multiple Robot Manipulation Tasks with Imperfect Demonstrations

Deep reinforcement learning (DRL) has been considered to be an effective method when dealing with robot control such as dexterous manipulation. With the increasing complexity of humanoid hand, control task is getting harder. Furthermore, to solve the drawbacks of DRL methods that may cause sample inefficient and slow exploration process, incorporating expert demonstrations have often been considered. Collecting demonstrations using deep learning methods will inevitably introduce errors, which forces the demonstrations to be imperfect. To solve that issue, we propose a weighting strategy ensuring that each demonstration will be used effectively and correctly, encouraging agent to be more efficient during learning. In the simulation experiments, with our weighting strategy and proposed unified reward function, our trained policies have achieved an average success rate over 85%, superior to the state of the art method in multiple manipulation tasks. We also validate that our method emerges more robust performance when using imperfect demonstrations with errors. The experimental results indicate that our method can be leveraged under different types of robotic environments.
Exploring code2vec and ASTminer for Python Code Embeddings

Automated understanding of code meaning and use has become an intrinsic part of software development recently. Neural models are being used in various natural language processing tasks, as they can represent natural language using vectors that carry semantic meanings. Although code is not natural language, we believe neural models to be capable of learning semantics and syntactic properties available in code snippets. To achieve such goal, we represent a code snippet using its abstract syntax tree (AST) syntactic paths to capture regularities that reflect common code patterns. This representation lowers significantly learning effort while being scalable to multiple problems and large code bases. In our work, we adopt ASTminer with code2vec, to represent code snippets as continuously distributed code vectors called "code embeddings", used to predict the semantic properties of the snippets. This approach decomposes code into a collection of AST paths and learns each path's atomic representation while learning how to aggregate them. Code2vec is then paired with other neural models, which represent query, to create a hybrid model for the task of code search. While code2vec was originally developed for Java only, we present in this article our efforts to extend the method to Python language.
Self-training and Label Propagation for Semi-supervised Classification

Due to the high cost of manually labeling data and sometimes requiring domain expertise, semi-supervised methods have received a lot of attention. Self-training is a very effective semi-supervised method that greatly improves the problem of insufficient labeled data in classification tasks. In this paper, we propose a semi-supervised classification algorithm based on self-training and label propagation. Specifically, our self-training architecture uses two soft pseudo-labels obtained by the fine-tuned model and label propagation as input to obtain the output of the pseudo-label prediction model, and then selects the high-confidence output of the pseudo-label prediction model as the pseudo-label data. Additionally, we use ImageNet pre-train models for fine-tuning, which greatly reduces learning time and improves accuracy. Experiments show that our method can achieve effective accuracy improvement on a large amount of unlabeled data.
DEDGraph: Delay Embedding of Dynamic Graph for Temporal Action Segmentation

In real-world interactive applications, where videos are generated in real-time and require immediate feedback, online segmentation has practical advantages over offline inference. Many excellent previous models have been developed for offline scenarios, while real-time prediction for temporal action segmentation (TAS) is a difficult task. Some interactive applications can tolerate a certain amount of delay. In this paper, we propose a node delay embedding of a dynamic graph for real-time TAS. We transform the video stream into a dynamic graph stream that evolves over time. We define past, current, and future nodes to construct sub-graphs at each step. Specifically, future nodes are sampled using our proposed node delay method. A graph model is utilized to aggregate past, current, and future node information to update the representation of current nodes and predict their labels. To the best of our knowledge, it is the first real-time TAS graph model with delay embedding. Experiments show that delay embedding enhances node representation and improves performance. Overall, our proposed approach provides a promising solution for real-time TAS.
IDP: Image Denoising Using PoolFormer

Recently, transformer-based models have achieved significant success in various computer vision tasks, with the attention-based token mixer module commonly believed to be the key factor. However, further research has shown that the attention-based token mixer module in transformers can be replaced by other methods, such as spatial multilayer perceptrons (MLPs) or Fourier transforms, to mix information between different tokens without sacrificing performance. Therefore, some have raised whether the success of transformers and its variants is not solely due to the attention-based token mixer module but rather to other factors. In a recent paper titled “PoolFormer” the authors demonstrated that using a simple spatial pooling operation instead of the attention module in transformers can achieve competitive performance in object detection vision tasks. Based on this finding, we propose a low-computation model for image denoising based on the PoolFormer and an MLP + CNN Transformer decoder for image restoration. By reducing the computational complexity brought by the token mixer, the model still achieves a good peak signal-to-noise ratio (PSNR) in grayscale as well as in color image denoising. This suggests that, in low-level vision tasks such as denoising, simple attention modules can also achieve good results, particularly in grayscale image denoising.
Reducing Computational Requirements of Image Dehazing Using Super-Resolution Networks

The task of image dehazing (DH) is an indispensable part of Advanced Driver Assistance Systems. This study proposes a method that combines image super-resolution (SR) and DH, focusing on how the models for DH and SR can be combined to reduce the overall computational cost. The considered DH networks include AOD-Net and GridDehazeNet, and the considered SR methods include bicubic interpolation, SRCNN, FSRCNN, and a modified version of SRCNN proposed in this study to help reducing the overall DH computations. In addition, a joint network training strategy for DH/SR combination is proposed. The experimental results show that the proposed method can reduce the computational complexity by about 70% compared to the original DH network – GridDehazeNet, while effectively preserving the image quality and clarity.
Performance Evaluation of Indonesian Language Forced Alignment Using Montreal Forced Aligner

Manual segmentation is crucial for aligning words or phonemes between speech signals and their transcription. It is used in many applications, such as speech recognition, speaker diarization, and speech synthesis. However, because manual segmentation can be laborious and time-consuming, forced alignment algorithms are used to automate the process by aligning the speech audio to its corresponding text transcription at the phoneme or word level. To overcome this problem, one of the solutions is a forced alignment tool called Montreal Force Aligner. This tool has two requirements: a language acoustic model and a corresponding language dictionary. The challenges are determining a suitable recipe for training the model and achieving a good alignment with minimum error. In this study, training and evaluation are conducted for the Indonesian language using the Montreal Forced Aligner. A conversational corpus from a range of five to 30 speakers with various duration between one hour and two hours is used as an input to train the acoustic model. Then, the model is employed to align the conversational Indonesian corpus using the ASR-INDOCSC test set. Our findings include a recommendation training strategy for developing an Indonesian language acoustic model using the Montreal Forced Aligner tool, which measures the number of speakers and hours required.
Significant Weighted Aggregation Method for Federated Learning in Non-iid Environment

Federated learning provides a decentralized learning without data exchange. Among them, the Federated Average (FedAVG) framework is the most likely to be implemented in real world application due to its low communication overhead. However, this architecture can easily affect the efficiency of global model convergence when there are differences data distribution in individual user. Therefore, in this paper, we propose an aggregation strategy called significant Weighted feature aggregation method, in which the features with large variation are appropriately weighted at the server side to improve the model convergence speed even in not identically and independently distributed (non-iid) environments. As shown in our experiments, our approach had over 10% of improvements compared to the FedAVG.