Reviewerly/exHarmony huggingface dataset:
Second-order Derivative Optimization Methods in Deep Learning Neural Networks

Optimization technique in deep learning neural networks is crucial to improve the parameters learning capability. Due to the recent advancements in technology and computational power, second-order derivative optimization methods have become increasingly popular as they offer additional information regarding the curvature of an objective function. In addition, multiple strategies can be applied based on the Hessian computation and step-length controlling parameter derived from the optimization trajectory when training a deep neural network. This paper demonstrates the latest second-order derivative optimization methods and their corresponding weight update rules in chronological order. The reviewed methods comprise Newton’s method, conjugate gradient, Quasi-Newton, Gauss-Newton, Hessian-free, Kronecker-factored approximate curvature, stochastic diagonal approximate greatest descent, AdaHessian, complex-step directional derivative, and Shampoo algorithms. A comparative study in terms of advantages, limitations, and performance are highlighted among these second-order derivative methods. An overall conclusion and future works are drawn in the paper to provide insights into the future development of optimization techniques.
Multiple Object Tracking Based on Tracking Compensation for Low-Resolution Scenarios

Multiple object tracking is one of the critical directions in computer vision research. In the application of vision-based tracking methods, cameras are sometimes installed far from the targets to obtain a global view. There would be a large number of targets in the videos with relatively low resolution, which increases the difficulty of visual tracking. Applying existing tracking methods directly in such low-resolution scenarios will result in low recall and a large number of discontinued trajectory fragments, due to the instability of the target detection results. To alleviate the tracking performance degradation in low-resolution scenarios, a multiple object tracking method based on tracking compensation (MOT-TC) is proposed in this paper. A detector is applied to produce the candidate bounding boxes of the targets in the current frame. Then trajectories from previous frames are used to predict their states in the current frame. An assignment method is adopted to match the candidate bounding boxes to the predicted states. For the unmatched trajectories in the current frame, a single object tracking method for compensation is used to provide the target positions, which can increase the recall and reduce trajectory fragments. Meanwhile, a strategy based on the response map of single object tracking is designed to evaluate the tracking performance. Extensive experiments on low-resolution videos have shown that the proposed method outperforms the baseline and other state-of-the-art methods by a large margin.
SIFRANK Algorithm for Chinese Text Keyword Extraction Based on Dependent Semantic Feature Constraints

The SIFRANK algorithm uses part-of-speech TAG to extract noun phrases as candidate sets for keyword extraction, and then uses the pre-trained ELMo model to obtain the semantic vectors of the current sentence and candidate phrases respectively, and measures the semantic similarity of the two to obtain the final keyword extraction results. However, it is difficult to set the regular expression of the part of speech(POS) TAG to a suitable formula. If it is too strict, the candidate set will be missing and the expected candidate words cannot be obtained. If the compound part of speech constraints such as gerunds are selected, unreasonable compound words will be obtained, resulting in poor final results. Therefore, this paper proposes a keyword extraction method DS-SIFRANK which increases the constraints of dependent semantic features. On the basis of SIFRANK, the restriction of part-of-speech TAG is enlarged, the dependency relationship is increased, and the candidate words are re-segmented and dependency syntactic parsing is performed to reduce the influence of inaccurate compound words on the extraction results. The experimental results show that the F1 index of the DS-SIFRANK method on the test set reaches 0.4872, which is 0.096 higher than that of the SIFRANK method.
Comparison of Sequential Feature Selection Performance with Various Dimensional Data to Produce Optimal Classification

Feature selection is one part of preprocessing which aims to reduce data dimensions. This study aims to produce optimal performance of the best feature selection method implemented on low- and high-dimensional data. The feature selection methods used are Sequential Forward Selection (SFwS) and Sequential Backward Selection (SBwS) algorithms. Meanwhile, to test the results of the best feature selection algorithm using the classification algorithm Logistic Regression, Linear Regression, k-Nearest Neighbor (kNN). The low dimensional data test results show that the SFS algorithm with the kNN classifier and the Regression group has the best average score. The SFwS algorithm with Logistic Regression has the best average score in the high dimensional data set. The minimal number of feature selections resulted from the SFwS algorithm rather than the SBwF algorithm. Although it should have the same accuracy or number of selected features, in this study, the results were different except for SFS-Linear Regression. The highest average accuracy score for low dimensional data is the Wine dataset of 0.994 (SFS-kNN), and for high dimensional data is the Parkinson’s disease dataset of 1 (SFS-LGR). The least number of feature selections obtained from the SFwS-Logistic Regression algorithm is one feature with an accuracy score of 0.8083. The Sequential Backward Selection algorithm generally has a longer running time than the Sequential Forward Selection.
Robust Prediction of Copy-Move Forgeries using Dual-Tree Complex Wavelet Transform and Principal Component Analysis

Copy-Move Forgery (CMF) reorders a square in an image to conceal or add information. To distinguish between changing regions, technologies that reveal falsification are needed. This work uses dual tree complex wavelet transform (DTCWT) to deteriorate pictures for copy-move forgery detection (CMFD). After degradation, high sub-band images are partitioned into overlapping squares, and PCA is used to compute features for each block. Then, Euclidean separation distance is utilised to calculate the square block distance between consecutive pairs of square blocks to match similar pairings and indicate possible change zones. The proposed work is evaluated using UCID. The quantitative findings were compared by comparing recall, accuracy, True Positive Rate (TPR), and False Positive Rate (FPR). The suggested strategy’s photos are subjectively inspected. The findings show that the suggested strategy outperforms current strategies available in literature on all criteria.
Automatic Text Summarization of Madura Tourism Articles Using TF-IDF and K-Medoid Clustering

Tourism is one of the most popular article topics. Reading articles takes time, especially if you read many article documents. An automatic text summary system offers a solution to shorten the time reading articles. Text summaries are composed of essential sentences from documents that are less or more than half in length. The length of the summary result amount is determined by the summarizer by specifying a threshold manually. This study proposes that TF-IDF and K-Medoid perform Extractive automatic text summarization of Indonesian language documents. This method is expected to automatically determine summary result limits and get good accuracy in automatic text summarization. The TF-IDF algorithm is known to be able to generate text summaries based on the weights obtained for each sentence in the text. The K-Medoid method is a clustering method that functions to group sentences considered by the system to have the same meaning. This study uses data totaling 33 document texts taken from different web sources on the internet. To test the study’s summary results, expert respondents will manually correct the summary results from the system. From the results of the tests that have been carried out, the accuracy value is 65.40%.
Loose Error Bounds and Exponential Convergence Rates of GMDS-ZNN for Time-Dependent Inverse Computing

Time-dependent matrix processing, especially the time-dependent inverse computing, plays an important role in mathematics science, control and industrial electronics fields. On the basis of previous work, Getz-Marsden dynamic system-Zhang neural network (GMDS-ZNN, or simply, GZ neurodynamics, GZND) has been shown as an effective neurodynamics method for the time-dependent inverse computing. In this paper, we analyze the stability and error properties of two GMDS-ZNN models for the time-dependent inverse computing by Lyapunov stability theory. As results, we present four theorems that describe the loose error bounds and exponential convergence rates of two GMDS-ZNN models for the time-dependent inverse computing. Moreover, we present two corollaries that describe the properties of the unit balls of the corresponding residual errors as well as solution errors of GMDS-ZNN models, which may be useful for proving the zero vanishing of solution errors in the future.
Selection Modification of Model in Profile Matching Algorithm for Admission and Placement of Assistant Members

Practicum assistants assist practicum supervisors in practicum activities in the subjects being taught. A practicum assistant must have the skills and criteria needed to carry out a practicum assistant’s duties so that students can participate appropriately in practicum activities. This study aims to select and place students to become electrical machine practicum assistants. The problem of selection is many rules with the complexity of student data. This study uses Profile Matching, which uses model development to improve accuracy in determining student placement as an electric machine practicum system. The tests in this study tested the accuracy in selecting students as assistants and tested the placement accuracy. This test obtained assistant selection accuracy of 86.6% and placement accuracy according to the Division of 83.3% using three models. Meanwhile, at the division placement stage, the accuracy rate obtained is 83.3%, with a weighting percentage of each Core Factor and Secondary Factor of 70% and 30%.
PDP-CNN: A Deep Learning Model for Post-Hurricane Reconnaissance of Electricity Infrastructure on Resource-Constrained Embedded Systems at the Edge

Unmanned aerial vehicles (UAVs) equipped with cameras have provided new capabilities for the reconnaissance of disaster-stricken areas. Deep learning-based computer vision algorithms enable the analysis of the captured images and the detection of damage to the built environment. If such analyses are conducted onboard in UAVs, they will provide real-time actionable information that is critical for the accelerated restoration of systems. However, conventional deep learning algorithms are computationally demanding. Moreover, the deployment of deep learning models with a large number of parameters in scenarios that require low-latency inference is prohibitive. To address this fundamental gap, we develop an efficient deep learning-based computer vision model of power distribution poles (PDPs) damage detection that is capable of onboard deployment in UAVs. Specifically, we propose a lightweight convolutional neural network (CNN) architecture called PDP-CNN that embodies multiscale feature operations and anchor-less object detection. This model is applied to a dedicated image database from a post-hurricane reconnaissance in PDPs. Results of extensive experiments show that PDP-CNN is capable of achieving high throughput, competitive accuracy, and efficient memory utilization on power-constrained embedded systems.
SonarGuard: Ultrasonic Face Liveness Detection on Mobile Devices

Liveness detection has been widely applied in face authentication systems to combat malicious attacks. However, existing methods purely depending on visual frames become vulnerable once visual perception is not reliable. The emerging face spoof and forge techniques urge the systems to exploit the defensive potential of non-visual modalities. To tackle this challenge, we introduce SonarGuard, a system combining ultrasonic and visual information to achieve robust liveness detection on mobile devices. More specifically, SonarGuard simultaneously extracts micro-doppler signatures from ultrasound reflections and motion trajectories from video frames both corresponding to the user’s lip movement. To further confirm the collected ultrasonic and visual information is not derived from malicious audio/video attacks, we consolidate the system via introducing a cross-modal matching mechanism, which demands the inherent consistency between these two modalities. Extensive experiments on a new dataset collected with existing mobile devices demonstrate that the proposed system could achieve average classification error rate of 0.91% under presentation attacks. This result indicates that SonarGuard can boost the security of face authenfication systems in real world usage without additional hardware modification.
Multi‐task learning with contextual hierarchical attention for Korean coreference resolution

Abstract Coreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks‐based coreference resolution for Korean using multi‐task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head‐final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word‐ and sentence‐level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre‐trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure.
Fall detection based on lightweight OpenPose algorithm

Falls is one of sudden, unintentional changes in body position, which usually occur in the elderly and may seriously affect the physical and mental health of the elderly. Thanks to the rapid development of computer vision algorithms, fall detection research based on RGB images or videos has gradually become the mainstream framework for fall detection due to its rich semantics, low cost, and friendly user experience. In this paper, as a means of improving the accuracy of fall detection in real time, a lightweight fall detection method based on Lightweight OpenPose is proposed. Specifically, the proposed method first calculates the skeleton map and joint point coordinates of the human body in real time based on the Lightweight OpenPose. Then, the obtained coordinates are combined with the proposed fall detection algorithm for detection. Through extensive experiments, we qualitatively and quantitatively verify the effectiveness of our method. At the end of this paper, we also put forward some views on the further improvement measures of this algorithm.
A Petri Net Architecture for Real-Time Human Activity Recognition in Work Systems

Real-time human-centered assistance in industrial processes depends on the individual history of the work person's activities in the work system and requires adequate methods for tracking the person's actions. Most research in human activity recognition is based on recognizing actions from video data using computer vision methods. Digital equipment, standardized machine data interfaces, and smart wearable devices extend the possibilities to describe the current state of the work system. Petri nets have already been applied to human activity recognition, however, without the requirement of detecting actions in real-time. This paper proposes a Petri net architecture that enables hierarchical description-based human activity recognition in industrial work processes. We present an extension, a Partitioned Colored Petri Net, based on the colored Petri net formalism that infers activities from state transitions of the work system in real-time. In a case study, we demonstrate the Petri net's application for an error-based learning system that visualizes error consequences in augmented reality using experimentable digital twins.
Systematic mapping study on the security and efficiency of blockchain in industrial context

In recent years, interest in Blockchain has increased dramatically. Blockchain technology is expected to play a significant role not only in the economy. The aim of this literature study is to shed light on the exact state of research on the following questions. It will be clarified whether there are open research areas in data security and access guarantee, efficiency and efficiency increase as well as features and characteristics in the years from 2016 to 2021, especially in relation to Industry 4.0. Special attention is paid to papers that can already serve with tests, simulations or source code. In the process, 27 papers in the category of blockchain were examined and systematically evaluated for data security, efficiency and information on the features of blockchain. As digitalisation develops, the security of systems and data is becoming increasingly important. Equally, however, performance should not suffer under security mechanisms. This mapping study provides an indication of how and whether blockchain can offer a solution. The main contribution in this study is a verification of the outcomes by presenting a case study. We highlight a practical example for an auditing process within a discrete manufacturing setup.
Selective User Plane (UP) Security for Throughput Enhancement in Mobile Communication

User plane (UP) security is used to provide integrity and confidentiality of user data between user equipment (UE) and radio access network (RAN). However, UP security significantly degrades UP throughput because it consumes notable processing resources to compute complex cryptography algorithms in packet data convergence protocol (PDCP) at UE and RAN. In this paper, we propose `Selective UP Security,’ which is application security-aware UP security to reduce the processing overhead of duplicated security protection and to enhance both UE and RAN throughput. In order to eliminate duplicated security of the PDCP packet, Selective UP Security firstly identifies the encrypted application traffic flow and then applies additional security to non-encrypted parts of the PDCP packet. We build a network testbed to evaluate our proposed scheme by utilizing open sources including UERANSIM, OpenAirInterface (OAI), and Open5GS. Experiment results demonstrate that our scheme can reduce security processing overheads of the PDCP layer by approximately 80% and processing overhead by approximately 30% at gNB and UE, and provide UP throughput enhancement by approximately 40%.
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient

Federated Learning (FL) plays a prominent role in solving machine learning problems with data distributed across clients. In FL, to reduce the communication overhead of data between clients and the server, each client communicates the local FL parameters instead of the local data. However, when a wireless network connects clients and the server, the communication resource limitations of the clients may prevent completing the training of the FL iterations. Therefore, communication-efficient variants of FL have been widely investigated. Lazily Aggregated Quantized Gradient (LAQ) is one of the promising communication-efficient approaches to lower resource usage in FL. However, LAQ assigns a fixed number of bits for all iterations, which may be communication-inefficient when the number of iterations is medium to high or convergence is approaching. This paper proposes Adaptive Lazily Aggregated Quantized Gradient (A-LAQ), which is a method that significantly extends LAQ by assigning an adaptive number of communication bits during the FL iterations. We train FL in an energy-constraint condition and investigate the convergence analysis for A-LAQ. The experimental results highlight that A-LAQ outperforms LAQ by up to a 50% reduction in spent communication energy and an 11% increase in test accuracy.
Incentive Mechanism for Federated Learning based on Random Client Sampling

Federated learning (FL) is a distributed machine learning paradigm that enables edge devices to participate in training as clients, and at the same time protect their privacy. Recent research in this field mainly focuses on improving training performance and reducing communication costs However, how to incentivize clients of federated learning still remains a challenge. Existing researches on FL often assume that clients participate in training voluntarily, which is not practical in most cases due to computation costs. In this paper, we propose an incentive mechanism for federated learning based on random client sampling. The mechanism consists of two parts First, a subset of clients are selected randomly according to the importance sampling scheme. Then, the interaction between the server and the subset of clients is modeled into a Stackelberg game. The server releases a total incentive, and the incentive is allocated to all clients based on their contribution. Clients then decide their choices of batch size, which potentially affects the contribution metric. Moreover, we prove that the client-level subgame of the Stackelberg game has a subgame equilibrium and can be written into a semi-closed form. We also propose an approximation algorithm for computing the subgame equilibrium of the server’s level subgame, which is shown in the experiment to converge to the equilibrium point successfully. The simulation results also demonstrate the effectiveness of our mechanism in comparison with two baselines.
Image Semantic Communications: An Extended Rate-Distortion Theory Based Scheme

Semantic communication is a promising paradigm for future artificial intelligence-empowered communications. In this paper, we propose a task-oriented semantic communications with semantic reconstruction (TOSC-SR) scheme for image semantic communications. Different from existing studies focusing on minimizing the pixel-level or feature-level distortion of images, TOSC-SR aims to preserve the semantic information of the image so as to ensure the performance of downstream artificial intelligence (AI) tasks. To this end, a new form of rate-distortion is first derived, which takes the semantic information related to AI tasks into consideration. We formulate TOSC-SR as an extended rate-distortion optimization problem. Then we propose a joint source and channel coding (JSCC) based semantic communication system, which are followed by multiple AI tasks. Experimental results show that the proposed approach outperforms the traditional JPEG, JPEG2000-based communication system and deep learning based multi-task communication system in terms of image reconstruction quality, AI task performance, and multi-task generalization ability.