Reviewerly/exHarmony huggingface dataset:
A 2.5 pJ/bit PVT-Tolerant True Random Number Generator Based on Native-NMOS-Regulated Ring Oscillator

In this brief, an energy-efficient true random number generator (TRNG) is presented based on the jitter noise of a single ring oscillator (RO). By XORing two different oscillation stages of the RO, consecutive pulses can be generated due to the fixed intrinsic propagation delay and more importantly, the time-varying jitter-noise-caused variation to the above delay. By continuously quantizing the above pulses’ width using another high-frequency RO-based time-to-digital converter (TDC), the TDC’s least significant bits (LSBs) are dominated by the jitter noise, of which the magnitude can be largely increased through reducing the RO’s supply voltage with a native NMOS transistor. Based on a 40-nm standard CMOS process, the proposed TRNG design is fabricated and extensively characterized. By passing the widely-exploited test tools of National Institute of Standards and Technology (NIST) and autocorrelation function, the proposed TRNG’s randomness has been well validated. Moreover, the randomness evaluation is further extended to various process-voltage-temperature (PVT) conditions, and the measured Shannon entropy is always larger than 0.999995, showing excellent PVT tolerance. Finally, the proposed TRNG features a high energy efficiency of 2.5 pJ/bit at a throughput of 53 Mbps.
Secure Distributed Storage Orchestration on Heterogeneous Cloud-Edge Infrastructures

Distributed storage systems spanning across different cloud data centers have substantially improved availability and flexibility for data storage and retrieval operations. However, stringent latency requirements of emerging applications necessitate optimized selection of storage resources that exhibit smaller delay. Introducing edge resources into distributed storage systems enables data placement closer to its source, but simultaneously increases the complexity of decision-making and orchestration processes for optimal data placement. In this work, we develop mechanisms for storing data across an infrastructure that includes both edge and cloud resources. Our approach focuses on optimizing data integrity, longevity, security, and cost, while leveraging erasure coding when performing the resource allocation. We first present a comprehensive mixed integer linear programming formulation of the storage resource orchestration problem. As the search space for the optimal solution can be vast and the execution time prohibitively large for real size problems, we also propose an innovative multi-agent heuristic approach that uses the rollout, a reinforcement based policy, to balance performance and execution time efficiently. Through various simulation experiments, we evaluate the developed mechanisms and trade-offs involved in our approach. By incorporating data from a multi-cloud provider, we further enhance the validity of the simulations and the conclusions drawn.
Graph Neural Network-Based Bitcoin Transaction Tracking Model

In recent years, with the rapid development of the digital economy, digital currencies such as Bitcoin and Ethereum have become increasingly popular among the public. Tracking and regulating digital currency transactions have become a challenging technology for the healthy development of the digital economy. That is because blockchain and peer-to-peer networks are the underlying technologies of digital currencies. Blockchain transaction has some new features, such as stronger anonymity and distributed storage. Therefore, it is difficult for the regulatory system to track the transaction relationships among users. Recent studies have shown that the accuracy, time, and space costs of transaction tracking, as well as the trade-offs between them, still need to be improved. In this article, we propose a new blockchain transaction tracking model called BT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Bitcoin Transaction Tracking Model). BT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> first combines an improved sampling aggregation algorithm with a graph neural network. And then, it exploits an inductive aggregation method to effectively generate rich node embeddings for a small number of unobserved nodes. Next, the node embedding vectors are used to generate edge information among nodes through message-passing functions. Finally, we can use the edge information to obtain the relations among Bitcoin accounts. This paper evaluates the model on the real-world dataset and explores the impact of various parameters, such as network depth and iteration time, etc. From the experimental results, the model’s average AUC (area under the ROC curve) can reach up to 0.93, and the average accuracy is 86%. The numerical results indicate that the performance of BT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is better than the state of art methods.
MonoIS3DLoc: Simulation to Reality Learning Based Monocular Instance Segmentation to 3D Objects Localization From Aerial View

3D object detection and localization based on only a monocular camera always faces its fundamental ill-posed issue to estimate 3D information. In combination with deep neural networks, recent researches have shown encouraging results to tackle this issue. However, most of them are only applied to street-view cameras based on several available small-size datasets and the 3D prediction accuracy of these methods is still low in comparison to traditional estimation methods using stereo-cameras. With the development of drone delivery applications in city spaces, it is also necessary to have a similar method to detect objects and estimate their 3D position from an aerial view. We proposed a novel Simulation to Reality approach to predict the object’s 3D position from an aerial view. An instance segmentation of an object is used as an intermediate representation not only to create a very large dataset for training by simulation but also to minimize the gap between simulation and reality. We designed a feed-forward neural network to predict the 3D position from instance segmentation and integrated it with a range-attention classification to improve accuracy, especially for 3D object detection at far distances. To evaluate our methods, we created two simulation datasets: one for cross-validation with other state-of-the-art methods and the other one for practical experiments on a real drone with a monocular camera. The experiment’s results demonstrate that we not only achieve better accuracy than the state-of-the-art methods using the monocular camera by testing on the same KITTI-3D dataset but also reach close to the accuracy of a stereo-based technique. Since our model is lightweight, we successfully deployed it on a companion computer on the real drone and the results of practical experiences are promising.
Structured SRS for e-Government Services With Boilerplate Design and Interface

There are many projects being carried out to develop e-Government applications. In order to develop an efficient application, a proper requirements specification is strictly required. However, in the case of improper requirements specifications, errors are unavoidable for the developed applications. Therefore, it is necessary to use a model to determine the quality of software requirements. In the literature, several studies have been presented to propose a quality model for software requirements. Yet, there is no study on the development of a quality model for software requirements in Turkish language. Thus, the purpose of this study is to propose a quality assessment model of Software Requirements Specification (SRS) in Turkish language for e-Government applications. The proposed model aims at defining common texts and confirming that the sentences used in writing SRS are developed within an assured structure in order to minimize and standardize the errors originating from natural language. For this purpose, a model based on the Rupp’s boilerplate is created that allows the analyst or requirements engineer to accurately enter the requirements statements. In this study, an interface (webpage) is also created so that the model may match requirements with common text templates, compute similarity values, and also may insert the requirements, regulatory documents, and user requirements into the template in a more convenient format. In order to evaluate the proposed model, the sentences in the 32 documents, including 843 requirements, were adapted to the model. Then, the usability of the model was validated by the requirement engineers serving in a Government service. According to the results, it is concluded that the proposed model is applicable, and is able to improve the quality of SRS in Turkish language for e-Government applications.
Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps

Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks such as depth/disparity estimation. Per-pixel depth/disparity maps can be used to enhance immersive multimedia applications such as 3D reconstruction and LF editing. Although disparity maps can be estimated for all LF views, most existing methods merely estimate depth/disparity for the central view and do not adequately deal with other LF views. To overcome this limitation and to exploit the advantage of recently proposed deep learning methods that estimate depth/disparity only for the central view, in this paper, an efficient and occlusion-aware disparity propagation method is proposed. The proposed method generates disparity maps for all LF views given a single disparity map for one reference view (e.g., the central view). The disparity map for the reference view is propagated into the four corner views and an existing disparity estimation model is used to fill any remaining holes in the corner views. Finally, disparity maps for remaining views are generated through a recursive and fast propagation. The proposed method computes not only disparity maps for all LF views but also handles occlusions and ensures angular consistency. Experimental results on synthetic and real LF datasets with different disparity ranges show outperforming or competitive results compared to the existing LF disparity estimation methods with a significant complexity reduction.
A Runtime Switchable Multi-Phase Convolutional Neural Network for Resource-Constrained Systems

Convolutional Neural Networks (CNNs) are widely used in various systems, including resource-constrained embedded systems or IoT devices. In such systems, it is typical to deploy compressed or pruned CNNs, instead of original ones, at the cost of reduced accuracy. Existing CNN pruning techniques have primarily focused on minimizing resource requirements. However, today’s embedded systems are increasingly dynamic in both resource demands and availability. Thus, the previous techniques that only consider given static cases are no longer efficient. In this paper, we propose a novel multi-phase CNN that enables a multi-objective exploration of a number of pruning candidates out of a single CNN. In the proposed technique, a CNN can operate in various versions depending on which subsets of weights are used and can be transformed to the one best matches to the given constraint adaptively and efficiently. For that, a CNN is first pruned to the sparsest form; then a set of parameters (sub-network) is additionally supplemented as the phase goes by. As a result, a number of network versions for all different phases can be represented by a single network and they form a pareto solution over the accuracy and resource usage trade-off. In this work, we target CPU-based CNN inference engines as most embedded systems do not have the luxury of specialized co-processor support such as GPUs or HW accelerators. The proposed technique has been implemented in a publicly available CPU inference engine, Darknet, and its effectiveness has been validated with a popular CNN in terms of design space exploration capability and runtime switchability.
A Reversible Multimedia Representation Method and Its Applications in Multimedia Processing

The single type of multimedia data, such as text, image, video, and audio, has been extensively studied in the past decades. With the advances in multimedia applications and services, the exponential growth of multimedia data has emerged. Therefore, it is crucial to effectively represent multimedia data and capture the relationships among them in the study of multimedia applications. In the paper, we propose a novel reversible multimedia representation method based on a complex base. In the proposed multimedia representation method, we first process different modalities of data, such as text, image, and audio separately and convert them into different Gaussian integers, then place them on the same representation plane and resulting in a joint multimedia representation model. Therefore, the relationships of multimedia data are established by using the geometric operations on the same representation plane. Experimental results show that the proposed multimedia representation method is lossless and reversible. In addition, we explore the potential applications of the proposed representation method in multimedia camouflage and multimedia secret sharing, and verify that the proposed representation method has good practical applicability in multimedia security.
A Privacy-Preserving Service Framework for Traveling Salesman Problem-Based Neural Combinatorial Optimization Network

The traveling salesman problem (TSP) is one of the classic combinatorial optimization problems, which can be widely used in intelligent transportation and logistics field. Neural network has shown great potential in combinatorial optimization tasks. However, it faces privacy leakage when a TSP neural combinatorial optimization network and user's data are directly outsourced to a cloud platform to provide and request service. In order to address the issue, this article proposes a privacy-preserving service framework for a TSP-based neural combinatorial optimization network called PPSF. We first protect the service provider's model parameters and users’ data by different split methods in multiple cloud servers, providing a secure outsourced mode for the participators in the PPSF framework. Then, in the secure outsourced mode, a series of secure computation protocols are designed for the cloud servers to support performing the secure computing in each service task. Moreover, it can also protect the process that the cloud servers respond to users after data processing and achieve private service result recovery. Finally, we prove that the proposed framework can realize privacy protection for TSP-based combinatorial optimization service and verify its utility and efficiency by experiments.
End-to-end speech recognition with reinforcement learning

In recent years, end-to-end automatic speech recognition (ASR) based on deep neural networks become popular because of its simple pipeline and excellent performance. However, there exists a main mismatch between its training and testing that might lead to performance degradation: in the training stage, existing method use the maximum likelihood criterion which aims to maximize log-likelihood of the training data, while in the testing stage the performance is evaluated by word error rate (WER), not log-likelihood. In this paper, we propose an alternative method based on reinforcement learning to make the goals of training and testing more consistent. Viewing speech recognition as a sequence decision process, the encoder-decoder based neural network is used as the policy function. The encoder is a pre-trained speech representation model (Wav2vec2.0), which generates the environment state encoding. The decoder is trained using a policy gradient algorithm based on a mix reward function which reflects both the word error rate and language model score. Experimental results on the LibriSpeech corpus show that our proposed method achieves 4% relative improvements over the baseline with a language model in terms of WER.
Video face recognition based on 3D chaotic attractor

In this paper, we propose an image feature extraction method based on 3D chaotic attractor, and carry out face video recognition. By adjusting the auxiliary function, the feature point set is located on a plane in the 3D coordinate system. The experiment shows that the feature point set on the plane can extract face features better and recognize faces more efficiently. This method is faster and has higher recognition rate than the method that uses trigonometric function to iteratively generate image feature point set to recognize face in video.
Ability to Write Argumentative Paragraphs of Class X Multimedia 1

Purpose of the study: The purpose of the study was to describe the ability to write argumentative paragraphs, class X Multimedia 1 students of SMK Negeri 9 Muaro Jambi in the 2016/2017 academic year by applying conditions such as, (1) aspects of paragraph criteria which include introduction, body of argument, and conclusions, (2) paragraph aspects such as unity, coherence and development, (3) linguistic aspects which include grammar, word choice, and punctuation.&#x0D; Methodology: This research uses a descriptive-quantitative method. The instrument used is the argumentative paragraph writing test. The subjects of this study were 24 students in class X Multimedia 1 at SMK Negeri 9 Muaro Jambi. The research instrument was in the form of an argumentative paragraph writing test.&#x0D; Main Findings: The results showed that in general the ability to write argumentative paragraphs of class X Multimedia 1 SMK Negeri 9 Muaro Jambi was quite capable. The details are (1) the ability to apply argumentation criteria which include introduction, body of argument, and conclusions with a value of 4.3 (able), (2) the ability to apply the requirements of a good paragraph includes unity, coherence, and development with a value of 3.7 (quite capable ) and (3) the ability to apply linguistic requirements which include grammar, word choice, and spelling and punctuation with a score of 3.6 (quite capable).&#x0D; Novelty/Originality of this study: Based on this conclusion, it is suggested to Indonesian language teachers at SMK Negeri 9 Muaro Jambi to improve learning to write argumentative paragraphs. Especially the ability to apply criteria such as, (1) aspects of argumentation paragraphs including introduction, body of arguments and conclusions, (2) aspects of good paragraphs include unity, coherence and development, (3) linguistic aspects which include grammar, choice of words, spelling and marks read.
Improving the effect of low-resolution face images output in AnimeGAN

In this paper, a novel approach for improving the output cartoon-style effect of low-resolution face images in AnimeGAN is proposed, which is an useful and effective method to generate high-quality cartoon-style face images. This new approach I proposed combines generative adversarial networks (GAN), AnimeGAN and SRGAN. The previous existing methods do not give satisfactory results on processing low-resolution images. The cartoon-style images generated from the LR images have many significant visual issues. For example, the output cartoon-style faces have some unreasonable and weird shadows and wrinkles, which is unreal and far from the effect of original images. In this paper, I introduce a new method of using SRGAN to increase the resolution of the original LR images in order to improve the output effect in AnimeGAN. At last, the experimental results show that my combined method has good output from LR images and improves the cartoon-style faces effect as well as the performance of AnimeGAN.
Infrared target tracking based on transformer

Infrared target images have low signal-to-noise ratio, blurred edges and missing textures, which make it a great challenge to identify the target and achieve stable tracking in the tracking process. However, ordinary target trackers use feature fusion as a convolutional operation, which is a local matching process that easily leads to the absence of high-level semantic information of the image, and is further limited on infrared images. Inspired by transformer, its attention mechanism can capture global features, as well as contextual relationships between features, and can well establish the association between remote features , long-range dependency and other advantages, we designed transofmer-based infrared target tracker, which is a network that performs feature enhancement and fusion on infrared images by tranformer, and classifies and regresses targets by classification head, and has proved the effectiveness of the method by conducting extensive experiments on challenging benchmarks.
Network traffic classification based on multi-head attention and deep metric learning

Network traffic classification plays an important role in network resource management and security. The application of encryption techniques and the rapid increase in the size of network traffic have placed higher demands on traffic classification. In this paper, we design multi-headed attention (MHA) and deep metric learning (DML) in our model for network traffic classification. In addition, MHA-DML also extracts more subtle and highly differentiated features through the improved triplet measurement loss. Experimental results demonstrate that the model achieves the best classification on all three publicly available web traffic datasets. The MHA-DML guarantees detection accuracy even when facing a classification task with many categories.
Design of study early-warning system based on machine learning

How to predict the students’ learning trend, according to the current situation of students' learning, is meaningful for the management of students' learning process. This paper presents a study early-warning system based on machine learning, using the method of machine learning with the data related to the scores of students’ professional courses, retaking percentage, graduation rate and bachelor's degree data of three classes of School of Information Engineering of Wuhan Business University, which establish a prediction model of student learning trend evaluation (whether there is a risk of not obtaining the degree). It provides reference for college students' study management and can improve the quality and efficiency of teaching management.
Sentiment analysis of online public opinion based on CNN-BiLSTM and attention mechanism

Emotion recognition from social network texts aims to mine netizens’ subjective emotions, such as stances and emotional tendencies, over an event, which is imperative for monitoring Internet public opinion. Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are widely used in the field of text classification. The combination of the two can exploit the CNNs’ feature extraction ability and BiLSTM’s ability to extract context dependency. However, for emotional recognition, it is also necessary to consider some specific words in online social text. Therefore, we constructed a model for emotion recognition based on Internet public opinion in three steps. First, the CNN was used to extract local features of social network texts. Second, context-related global features were extracted using the BiLSTM. Finally, we introduced an attention mechanism to obtain important features. Experiments were conducted using netizens’ comments from a microblog during the COVID-19 epidemic as the dataset. Experimental results showed that the feature vector of the proposed model (i.e., the CNN-BiLSTM-Attention model) contains richer semantic information of texts, which can effectively improve the performance of emotion recognition from Internet public opinions.