Reviewerly/exHarmony huggingface dataset:
A convolutional and transformer based deep neural network for automatic modulation classification

Automatic modulation classification (AMC) aims at identifying the modulation of the received signals, which is a significant approach to identifying the target in military and civil applications. In this paper, a novel data-driven framework named con-volutional and transformer-based deep neural network (CTDNN) is proposed to improve the classification performance. CTDNN can be divided into four modules, i.e., convolutional neural network (CNN) backbone, transition module, transformer module, and final classifier. In the CNN backbone, a wide and deep convolution structure is designed, which consists of 1 × 15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections. In the transition module, a 1 × 1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features. In the transformer module, three self-attention layers are designed for extracting global features and generating the classification vector. In the classifier, the final decision is made based on the maximum a posterior probability. Extensive simulations are conducted, and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.
<b>“</b>Known Unknowns”: Reducing Digital Inequalities in the Silver Economy

Because of the world's aging population, Europe's over-50 segment has grown significantly. This demographic trend has created a major economic opportunity known as the silver economy. Technology plays an important role in the development of products and services targeting this older market segment, yet technological innovation has compromised these individuals’ quality of life by aggravating already existing social disparities. Limited access to and use of technology is known as digital inequality, which is a matter of growing concern that is pushing experts to develop initiatives that mitigate the impact of this disparity. However, decision makers first need to be fully aware of the challenges associated with this endeavor, which arise out of the inherent complexity of digital inequality and the uncertainty produced by technological disruptions. This study addresses this decision problem by adopting a constructivist, socio-technical approach that combines cognitive mapping and decision-making trial and evaluation laboratory techniques in a neutrosophic context. Data were collected from an expert panel with experience in real-world cases. The members analyzed different strategies and their effectiveness in terms of reducing digital inequalities in the silver economy. The results were analyzed and validated by the Head of Data and Analytics at Nippon Telegraph and Telephone Data Europe and Latin America. No prior research was found that has used the same combination of operational research methods to address this decision problem. The findings include recommendations for how to facilitate the development of initiatives that increase digital literacy in a silver economy context.
LineDL: Processing Images Line-by-Line With Deep Learning

Although deep learning-based (DL-based) image processing algorithms have achieved superior performance, they are still difficult to apply on mobile devices (e.g., smartphones and cameras) due to the following reasons: 1) the high memory demand and 2) large model size. To adapt DL-based methods to mobile devices, motivated by the characteristics of image signal processors (ISPs), we propose a novel algorithm named LineDL. In LineDL, the default mode of the whole-image processing is reformulated as a line-by-line mode, eliminating the need to store large amounts of intermediate data for the whole image. An information transmission module (ITM) is designed to extract and convey the interline correlation and integrate the interline features. Furthermore, we develop a model compression method to reduce the model size while maintaining competitive performance; that is, knowledge is redefined, and compression is performed in two directions. We evaluate LineDL on general image processing tasks, including denoising and superresolution. The extensive experimental results demonstrate that LineDL achieves image quality comparable to that of state-of-the-art (SOTA) DL-based algorithms with a much smaller memory demand and competitive model size.
Rec-RN: User Representations Learning over the Knowledge Graph for Recommendation Systems

Collaborative Filtering, which is the main method for recommending items, suffers from low performance due to sparse interactions between items and users. A Knowledge Graph (KG), thus, is introduced to mitigate the problem above. To obtain high-quality user-item features, this paper represents user features by combining user history records with nodes in the KG, e.g, a user has seen the film “Cast Away featuring the actor Tom Hanks, who also appears in the movie” “Forrest Gump”; in the KG. Therefore, the user is highly likely to watch the film “Forrest Gump As a result, both movies can be used to represent a user’s preferences. Therefore, this paper proposes a KG-based model Rec-RN, an end-to-end recommendation systems framework. Three commonly used datasets, namely MovieLens-lM, Book-Crossing, and Last. FM, are evaluated. Our Rec-RN outperforms the classic algorithm in the click-through rates, with accuracy improvements of 2.2%, 3.1%, and 3.5% in the movie, book, and music fields, respectively.
Statistical analysis of the solar diffuse fraction radiation using regression analysis of longitudinal data in India

In this study, the validity of the estimation of a single regression equation for the diffuse frac-tion across 22 stations in India using the two parameters: the clearness index and the sunshine ratio is tested. The homogeneity test based on Fisher’s statistics was applied to test the homo-geneity of the estimated parameters across all stations. The results showed that the p-value at the level of 5% for each model is smaller than 0.05, indicating that all stations were heteroge-neous. The Hierarchical Cluster Analysis (HCA) was used to classify the data into homoge-nous clusters. The results of HCA indicated that the longitudinal data were divided into four main clusters. For each cluster, the regression analysis was applied based on the longitudinal data then, the fixed effects model (FEM) and the random-effects model (REM) were used for the evaluation. Further, the Hausman test was applied to choose between the fixed effects model and the random-effects model. Finally, the results showed that the four best regression models were found for the selected stations in the study area.
Multi-modal Structure-embedding Graph Transformer for Visual Commonsense Reasoning

Visual commonsense reasoning (VCR) is a challenging reasoning task that aims to not only answer the question based on a given image but also provide a rationale justifying for the choice. Graph-based networks are appropriate to represent and extract the correlation between image and language for reasoning, where how to construct and learn graphs based on such multi-modal Euclidean data is a fundamental problem. Most existing graph-based methods view visual regions and linguistic words as identical graph nodes, ignoring inherent characteristics of multi-modal data. In addition, these approaches typically only have one graph-learning layer, and the performance declines as the model goes deeper. To address these issues, a novel method named Multi-modal Structure-embedding Graph Transformer (MSGT) is proposed. Specifically, an answer-vision graph and an answer-question graph are constructed to represent and model intra-modal and inter-modal correlations in VCR simultaneously, where additional multi-modal structure representations are initialized and embedded according to visual region distances and linguistic word orders for more reasonable graph representation. Then, a structure-injecting graph transformer is designed to inject embedded structure priors into the semantic correlation matrix for the evolution of node features and structure representations, which can stack more layers to make model deeper and extract more powerful features with instructive priors. To adaptively fuse graph features, a scored pooling mechanism is further developed to select valuable clues for reasoning from learnt node features. Experiments demonstrate the superiority of the proposed MSGT framework compared with state-of-the-art methods on the VCR benchmark dataset. The source code of this work can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://mic.tongji.edu.cn</uri> .
Quality improvement method for high-end equipment’s functional requirements based on user stories

Aiming at problems such as incomplete, inconsistent, and inaccurate requirements that often occur in the process of obtaining high-end equipment functional requirements, this paper presents a requirement quality improvement method. Referring to the agile development theory of requirements engineering, the quality improvement method constructs a functional requirement model based on user stories, defines the concept of functional requirement quality, designs functional requirement quality evaluation criteria, and constructs a functional requirement quality evaluation process. A case study and sensitivity analysis of new energy vehicle requirement development are conducted to confirm the feasibility and effectiveness of the method, and the experimental results show the superiority of the proposed method in improving the quality of high-end equipment functional requirements.
An improved deep reinforcement learning for robot navigation

Collision-free navigation is an important research direction for multi-robot systems, in which the two core problems are navigating to the target point and avoiding other robots. Many researchers use deep reinforcement learning as navigation strategy to realize multi-robot collision avoidance navigation. However, most of them use raw sensor information or global state information of the agent as the neural network input, which is not conducive to extending the navigation strategy to a larger space. This paper proposes an improved deep reinforcement learning navigation strategy, which enables robots to learn navigation and collision avoidance strategies more accurately. This strategy converts the interactive environment state from the global coordinate representation to the relative vector representation, and attenuates the influence of the rear irrelevant agents on the collision avoidance strategy. Experimental results show that the proposed method outperforms existing learning-based methods in three indicators: success rate, additional time to reach the target, and model convergence speed.
Detecting quantum critical points at finite temperature via quantum teleportation

We show that the quantum teleportation protocol is a powerful tool to study quantum phase transitions at finite temperatures. We consider a pair of spins from an infinite spin-1/2 chain ($XXZ$ model) in equilibrium with a reservoir at temperature $T$ as the resource used by Alice and Bob to implement the teleportation protocol. We show that the efficiency of this pair of spins to teleport a qubit is drastically affected after we cross a quantum critical point (QCP), even for high values of $T$. Also, we show that the present tool is as sharp as quantum discord (QD) to spotlight a QCP, where QD is the best finite $T$ QCP detector known to date. Contrary to QD, however, we show that the present tool is easier to compute theoretically and has a direct experimental and operational meaning.
Query cost estimation in graph databases via emphasizing query dependencies by using a neural reasoning network

Summary With the increasing complexity of graph queries, query cost estimation has become a key challenge in graph databases. Accurate estimation results are critical for database administrators or database management systems to perform query processing or optimization tasks. An efficient and accurate estimation model can improve the estimation quality and make the produced results credible. Although learning‐based methods have been applied in query cost estimation, most of them are directed at relational queries and cannot be directly used for graph queries. Furthermore, most estimation approaches focus on the correlations between predicates or columns. The dependencies between query schema and query filter conditions and the correlation between query schema are ignored. In this study, we construct a novel deep learning model composed of reasoning and retrieval processes that can accurately capture the potential logical relationships in graph queries. This solves the above problems to some extent. In addition, we propose a query estimation framework that divides the estimation task into query workload generation, training data collection, feature extraction and encoding, and estimation model construction. The results of the experiment on real‐world datasets show that our estimation model can improve the estimation quality and outperforms other compared deep learning models in terms of estimation accuracy.
Detecting Data Outliers with Machine Learning

Anomalies are instances or collections of data that occur very rarely in a dataset and where features differ significantly from most of the data. In the age of technology, data is widely used in all sectors. Thus, anomalies in the data may produce problems if they are not detected. Anomaly detection involves examining specific data points and detecting rare occurrences that seem suspicious because they are different from the established pattern of behaviors. In this study, an approach to anomaly detection is built using a machine learning technique. The clustering distance-based method (k_means) is adopted. First, the anomaly existence is tested using p_value. After that, the anomaly data is detected using the clustering method. The proposed method was tested using real data collected from Kaggle. The results show the good performance of the k_means algorithm in the detection of outlier data.
Optimization of numerical weather model parameterizations for solar irradiance prediction in the tropics

Abstract Many photovoltaic (PV) systems are connected to electrical power grids, and the grids are at risk of instability due to fluctuation of PV output. Numerical weather prediction (NWP) models are used to forecast solar irradiance and proper grid management. NWP usually has many physical parameterization options, and appropriate schemes of these options should be selected for accurate forecasting. The options should be determined by regional and climatic conditions and other factors. The target country is Thailand, which is in the tropics. In Thailand, cumulus and cumulonimbus clouds frequently appear, and their behavior makes weather forecasting difficult. The optimal combination of schemes in the tropics is determined through a sensitivity analysis of the options. By the optimization the forecasting accuracy increases from 0.773 to 0.814 of the correlation coefficient. It is also found that surface layer and PBL processes make a significant contribution to the improvement of accuracy.
Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis

Abstract Objective. Multi-parametric MR image synthesis is an effective approach for several clinical applications where specific modalities may be unavailable to reach a diagnosis. While technical and practical conditions limit the acquisition of new modalities for a patient, multimodal image synthesis combines multiple modalities to synthesize the desired modality. Approach. In this paper, we propose a new multi-parametric magnetic resonance imaging (MRI) synthesis model, which generates the target MRI modality from two other available modalities, in pathological MR images. We first adopt a contrastive learning approach that trains an encoder network to extract a suitable feature representation of the target space. Secondly, we build a synthesis network that generates the target image from a common feature space that approximately matches the contrastive learned space of the target modality. We incorporate a bidirectional feature learning strategy that learns a multimodal feature matching function, in two opposite directions, to transform the augmented multichannel input in the learned target space. Overall, our training synthesis loss is expressed as the combination of the reconstruction loss and a bidirectional triplet loss, using a pair of features. Main results. Compared to other state-of-the-art methods, the proposed model achieved an average improvement rate of 3.9% and 3.6% on the IXI and BraTS’18 datasets respectively. On the tumor BraTS’18 dataset, our model records the highest Dice score of 0.793(0.04) for preserving the synthesized tumor regions in the segmented images. Significance. Validation of the proposed model on two public datasets confirms the efficiency of the model to generate different MR contrasts, and preserve tumor areas in the synthesized images. In addition, the model is flexible to generate head and neck CT image from MR acquisitions. In future work, we plan to validate the model using interventional iMRI contrasts for MR-guided neurosurgery applications, and also for radiotherapy applications. Clinical measurements will be collected during surgery to evaluate the model’s performance.
Magi-Net: Meta Negative Network for Early Activity Prediction

Early activity prediction/recognition aims to recognize action categories before they are fully conveyed. Compared to full-length action sequences, partial video sequences only provide insufficient discrimination information, which makes predicting the class labels for some similar activities challenging, especially when only very few frames can be observed. To address this challenge, in this paper, we propose a novel meta negative network, namely, Magi-Net, that utilizes a contrastive learning scheme to alleviate the insufficiency of discriminative information. In our Magi-Net model, the positive samples are generated by augmenting an input anchor conditioned on all observation ratios, while the negative samples are selected from a trainable negative look-up memory (LUM) table, which stores the training samples and the corresponding misleading categories. Furthermore, a meta negative sample optimization strategy (MetaSOS) is proposed to boost the training of Magi-Net by encouraging the model to learn from the most informative negative samples via a meta learning scheme. Extensive experiments are conducted on several public skeleton-based activity datasets, and the results show the efficacy of the proposed Magi-Net model.
Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology

The development of a comprehensive and decisive drone defense integrated control system that can provide maximum security is crucial for maintaining territorial integrity and accelerating smart aerial mobility to sustain the emerging drone transportation system (DTS) for priority-based logistics and mobile communication. This study explores recent developments in the design of robust drone defense control systems that can observe and respond not only to drone attacks inside and outside a facility but also to equipment data such as CCTV security control on the ground and security sensors in the facility at a glance. Also, it considered DDS strategies, schema, and innovative security setups in different regions. Finally, open research issues in DDs designs are discussed, and useful recommendations are provided. Effective means for drone source authentication, delivery package verification, operator authorization, and dynamic scenario-specific engagement are solicited for comprehensive DDS design for maximum security Received: 2023-03-07 Revised: 2023-04-27
Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model

Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manual feature generation and conventional shallow learning methods, which restrict generalization. As a result, the goal of this research is to develop a general approach using transfer learning by applying the “BERT (Bidirectional Encoder Representations from Transformers)”-based model. The efficiency of BERT classification is then evaluated by comparing it with similar machine learning techniques. In the experimental evaluation, the proposed model demonstrated superior performance in terms of outstanding prediction and high accuracy compared to earlier research. Comparative tests conducted on positive and negative Yelp reviews reveal that fine-tuned BERT classification performs better than other approaches. In addition, it is observed that BERT classifiers using batch size and sequence length significantly affect classification performance.