Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal GP Model Learning for Intention-Driven Motions

Most human activities and object motions in the real world are intention-driven. Taking advantage of the intention information (e.g., goals and destinations) can produce better motion models and more accurate trajectory prediction in general. Again, compared with the traditional state space models, Gaussian process (GP) based models have more capability to de-scribe complicated motions. This paper proposes a GP regression based approach to model learning and trajectory prediction for intention-driven motions. At first, the conditional kernels are devised by incorporating the known motion intent, from which it follows that the GP models of intention-driven motions are constructed. Then, the times at which the destination is reached, as key parameters for GP models with conditional kernels, are learned online based on the data stream. Finally, in the context of missile tracking, numerical simulations are provided to show the effectiveness of the proposed GP models and the self-learning ability of their hyper parameters for intention-driven motions.
A Certificate Authority Scheme Based on Trust Ring for Consortium Nodes

The access control mechanism of most consortium blockchain is implemented through traditional Certificate Authority scheme based on trust chain and centralized key management such as PKI/CA at present. However, the uneven power distribution of CA nodes may cause problems with leakage of certificate keys, illegal issuance of certificates, malicious rejection of certificates issuance, manipulation of issuance logs and metadata, it could compromise the security and dependability of consortium blockchain. Therefore, this paper design and implement a Certificate Authority scheme based on trust ring model that can not only enhance the reliability of consortium blockchain, but also ensure high performance. Combined public key, transformation matrix and elliptic curve cryptography are applied to the scheme to generate and store keys in a cluster of CA nodes dispersedly and securely for consortium nodes. It greatly reduced the possibility of malicious behavior and key leakage. To achieve the immutability of logs and metadata, the scheme also utilized public blockchain and smart contract technology to organize the whole procedure of certificate issuance, the issuance logs and metadata for certificate validation are stored in public blockchain. Experimental results showed that the scheme can surmount the disadvantages of the traditional scheme while maintaining sufficiently good performance, including issuance speed and storage efficiency of certificates.
Rethink Object Tracking Based on Transformer

Most visual tracking networks with transformers use attention mechanisms instead of convolutional structures. Few new methods have focused on comparing objects and search regions in visual tracking networks. However, the association information between the target in the first frame and the search area in subsequent frames will significantly affect the predicted results of subsequent targets. As a result, we propose a tracking structure: TEDTrack, which makes full use of the correlation between a given target and the search area of the following image. TED (transformer encoder-decoder, TED) is a feature fusion mechanism of TEDTrack, which mainly uses a transformer codec to integrate the extracted template features and the next frame search region features. It can achieve better results than the Previous single-target tracking algorithm which adopted the Siamese network. The tracker in this paper has conducted many experiments on multiple challenging short-term and long-term benchmark datasets. The experimental results show the effectiveness of TEDTrack.
Short Text Semantic Matching Model based on Bert and Adversarial Network

Short text semantic matching plays an important role in the fields of natural language processing such as fast retrieval, intelligent question answering, and information matching. Aiming at the problem of word polysemy that is difficult to solve by conventional models, this paper proposes a short text semantic matching model BERT-GAN based on the BERT (Bidirectional Encoder Representations from Transformers) pre-training model and combined with the adversarial network in the fine-tuning stage. The basic idea is as follows: Using BERT to extract text features, and then introducing an adversarial network in the fine-tuning stage of the task to add perturbation to the embedding layer to improve the generalization ability and robustness of the model. The experimental results show that the BERT-GAN short text semantic matching model is better than the comparison model, and the F1 value is improved by 10.5%, 6.6% and 0.9% respectively compared with the comparison model.
A Fair Federated Learning Framework based on Clustering

Federated learning is a distributed learning with multiple participants, and the cooperation and competition among participating clients tend to raise the issue of collaborative fairness. If the collaboration among clients is not fair, resulting in resources being monopolized by some advantaged clients, depriving vulnerable clients of participation opportunities, and even causing a “Matthew effect” on client learning resources. To address the above issues, we propose a fair federated learning framework based on clustering. First, we align vulnerable participants into clusters and increase their chances of participating in the federation. The distribution of the global model in clustering is according to the principle of “allocate by work” to balance contributions and profits. We transform the clustering problem into a Liapunov online queue scheduling problem by establishing a bi-objective optimization function. Finally, through theoretical and experimental analysis, the scheme is proved to have good performance in accuracy and fairness.
An Attributed Network Anomaly Detection Method Based on Dual Autoencoder Joint Embedding

Anomaly detection in attributed networks has attracted a lot of attention in recent years. It is an important means to detect and find security anomalies in time and to take measures in advance to combat threats. Most existing methods ignore the complex interaction between network structure and node attributes, so it remains a challenging problem to better model the network structure information and the rich node attribute information to achieve the interaction between the two models. In this paper, we propose an attributed network anomaly detection method based on dual autoencoder joint embedding (DAJE). This method learns the embedding of both structural and attribute patterns separately by using structural and attribute autoencoders, and then reconstructs them while considering the consistency and complementarity of the structural and attribute information embedding. It takes into account the network structure and attribute interactions better than other methods, and its effectiveness is verified on three real-world datasets.
A Novel Least Significant Bit Steganographic Method Based on Hough Transform

The world becomes a digital era and growth rapidly in technology, so transmission data secretly and securely becomes an essential topic. Many different techniques are available for securing transmission data and using images to embed secret data. Some of these techniques used Hough Transform in their technology. The main goal of these methods is embedding a huge amount of data into images with high level of imperceptibility. However, increasing the amount of embedded data into the image decreasing its quality. Therefore, this work propose a new steganography method based on Least Significant Bit (LSB) using Hough Transform to improve the quality of stego image with increasing the amount of embedded data. The proposed method inverts the LSBs of image pixels to improve the stego image quality. First, we detect edges areas by using improved edge detection filter. Then, we invert LSBs for the pixel in edge area pixels. Finally, the LSBs smooth area pixels of the cover image are inverted. The performance evaluation of the presented method is measured by the stego image quality and the amount of embedded data. The results show that the proposed has better peak signal to noise ratio (PSNR) and capacity in comparison with the current steganography methods.
Blind Signature Scheme Based on QC-MDPC Codes

In this paper, we construct a new blind signature scheme based on Quasi-cyclic Moderate Density Parity-Check (QC-MDPC) codes. This scheme is realized by using QC-MDPC codes and bit flipping decoding algorithm under Courtois-Finiasz-Sendrier (CFS) signature system. The security of our blind signature scheme relies on the difficulty of the syndrome decoding problem in coding theory, which has been shown to be NP-complete and secure against quantum attacks. This scheme greatly reduces the public key size as well as the signature length compared to other code-based blind signature schemes. To achieve security level 2^80, the public key size in our blind signature scheme is 4801bits and the signature length is 1029.05bits, which are better than almost other schemes.
Smart Contracts based on Hyperledger Fabric Blockchain for the Purpose of Higher Education Subsidizing

During their studies, many students drop out of one specialty, then apply and enroll in another. The state subsidized several semesters of their studies, then again subsidized the student's studies in another specialty. A number of students start but do not complete their studies, but this has been subsidized by the state as well. No research has been focused on the state's losses from subsidizing education in these cases. This paper proposes a model where any citizen can receive a subsidy for his studies, but only for up to 10 semesters. After that, he has to pay the full cost of his education. Under this model, students are motivated to complete their studies within the subsidized semesters, and the state does not suffer losses from refinancing the same student whose study period exceeds these semesters. The model is based on a smart contract on Hyperledger Fabric platform.
Outlier Rejection on Payload Metadata System Time of LAPAN-A3 Satellite using Sequential RANSAC

System time is one of the most pivotal metadata of a satellite payload, thus having outliers on the system time can lead to failure in the data processing. Therefore, an outlier rejection method is crucial to cleanse the metadata before partaking in further processes. In this research, three variants of the random sampling consensus (RANSAC) algorithm are selected to deal with outliers in the system time of the LAPAN-A3 satellite. Default RANSAC, RANSAC with least square, and RANSAC with median value are chosen to be tested to the synthetic data with a variation on the outlier ratio and standard deviation. RANSAC with the least square variant shows superiority in precision, recall, and robustness. Furthermore, a sequential RANSAC is built to deal with multiple structures on the metadata of LAPAN-A3. The method can estimate the outlier with a precision of 0.9 and a recall of 0.6.
Reconfigurable Cyber-Security Architecture for Small Satellite with Low Complexity and Power

Small satellites are extensively developed to serve a wide range of space missions, from exploration to defense. They are becoming a promising replacement for large spacecraft due to their low development cost and lead time. Small satellites are widespread in the Low Earth Orbit (LEO), and they are operated by governments, space agencies, universities, and companies. The communication between satellites and ground segments is vulnerable to different cyberattacks where attackers can access critical data that could jeopardize national security. Thus, satellite communication cybersecurity has become a crucial role in the sustainability of satellite systems, where some of these vulnerabilities can negatively impact the mission. The design of small satellites has many challenges such as low power consumption, limited data rate, and limited design area. This research paper proposes a revised onboard cybersecurity architecture for small satellites. A hardware implementation of an optimized Advanced Encryption Standard (AES) algorithm was implemented to optimize the design complexity and power consumption. The optimized AES algorithm was implemented using a reconfigurable Field Programmable Gate Arrays (FPGA) device Spartan-6 to evaluate the performance, resource utilization, and power consumption compared to existing Commercial off-the-shelf components (COTS). The results show high performance for securing data and images with low power consumption and low complexity, making it suitable for small satellites.
Rotation Model Enhancement for Adversarial Attack

Current white-box attack to deep neural networks have achieved considerable success, but not for black-box attack. The main reason is poor transferability, as the adversarial examples are crafted with single deep neural networks model, and excessively depend on that model. To address that problem, we propose a rotation model enhancement algorithm to craft adversarial examples. We improve rotation method in model enhancement. This algorithm constructs a possibility model to randomly rotate original images, and generates multiple transformed images. Therefore, we craft adversarial examples with single model, and boost attack on multiple models, which demonstrate considerable transferability and success rate for black-box attack. The simulation indicates the algorithm boost black-box attack with a 89.2% success rate.
Acceptance of Information on the Distribution of Smoke and Locations of Fire (HotSpot) Resulted from Remote Sensing Data

Forest/land fire is one of the causes of economic and health losses that have a massive impact. Forest/land fires can also disrupt relations between countries when the transboundary haze rises. Remote sensing data produces the fire hotspots (HotSpot) in daily observation. The HotSpot products are disseminated to users through a website or application with an android interface. This research examined the user responses in receiving HotSpot information using the approach Technology Acceptance Model (TAM) and Best Worst Method (BWM) in Indonesia. The results show that the factors influencing the acceptance of space-based smoke and HotSpot information are Attitude Toward System (ATT) and Facilitating Conditions (FC). In addition, the level of acceptance is at the level of being accepted by the user with a criterion weight index of 5.077 and a consistency value ratio (CR) of 0.024 < 0.4747.
FedDBG: Privacy-Preserving Dynamic Benchmark Gradient in Federated Learning Against Poisoning Attacks

The federated learning's (FL) ability to protect local data privacy while cooperatively training powerful global models has received extensive attention. Although some researchers have carried out researches on gradient privacy disclosure under poisoning attacks, the existing works still ignore the unreliability of initial data, which makes it difficult to obtain the benign initial reference gradient, resulting in a significant decline in the accuracy of the final global model. To solve this problem, we propose a privacy-preserving gradient framework in FL based on homomorphic encryption. The framework can ensure that malicious initial users and subsequent users cannot interfere with the accuracy of the global model by uploading the poisoning gradients. In this process, key parameters such as gradients of local users won't be leaked. We then design a dynamic reference gradient aggregation algorithm to mitigate the poisoning attack in FL, dynamically dividing the sub-gradients of each round of local uploads by clustering the gradients of different local uploads. Furthermore, the malicious and benign gradients are further separated and the optimal global model is obtained by iterative updating. We proved the security of the scheme theoretically, and verified the effectiveness of the scheme through experiments. The accuracy of the proposed scheme is at least 80% higher than that of the scheme without anti-poisoning measures.
Predictivity of Category Based Human Navigation and the Effect of Navigation Path Length on the Prediction Accuracy in Knowledge Networks

In web sites containing large amount of information, systems which help visitors access the contents they are interested in, significantly increase the usability of the sites. Knowing the category of the content that the user wants to access is important to offer more qualified suggestions and advertisements to the user. On the other hand, as the user continues to navigate in the site, more navigation data is obtained from him, which may potentially be used for better predictions.In this study, the predictability of users' category-based navigation and the effect of the length of the navigation path on the prediction accuracy in knowledge networks is investigated using LSTM classifiers.
NIS Test for Selecting the Order of Local Polynomial Model

This work presents an approach that uses normalized innovations squared (NIS) test to determine the order of a local polynomial trend model of a humidity sensor signal. Compared to the classical Akaike information criterion which balances model fit and model complexity NIS test allows to determine the lowest order of the model at which a consistent Bayesian estimation is possible. This approach is useful when signal processing algorithms, such as those for data fusion in multi-sensor systems, contain a Kalman filter, because a successful NIS test is a prerequisite for normal filter operation. The results of the study show that the response of the SHT31-DIS sensor under a step change in humidity is adequately described by a second-order local polynomial model, and this implies a consistent estimation for other input variations.
Graph Deep Clustering using Cluster Graph Conventional

Graph deep clustering aims to have an effective representation of the graph data structure into multiple groups. Most of the studies in graph clustering move to apply deep learning because of the effectiveness of the combination for feature extraction, dimensionality reduction and clustering. However, the graph area is very wide and always has a lot of varieties and possibilities for new approaches and models. The different types of graphs and networks in addition to the type of the data and graph structure cause that variety and the increase in the graph deep clustering models. Thus, it is important to gather all the new and latest approaches and compare the performance according to the most popular datasets in this field. This study focuses on the latest approaches in the area of graph deep learning clustering. Moreover, the best performance result approaches from 2016 until 2021 are compared according to the highest accuracy result. The comparison is done between Cora, Pubmed and Citeseer datasets for each approach. Since the data in the real world is complex, the study also focuses on the proposed approaches for an important type of graph which is a multiplex graph. The study investigates that most of the new approaches are using the node and graph structures in the learning method. Most of the approaches are evaluated on a single layer graphs, wheres few proposed using multiplex graph. Another main finding in this paper is the use of the features of the LSTM as an autoencoder techniques in the graph clustering. However, there are few studies in this field which can be an area for researchers as new studies. Finally, a cluster graph conventional model is proposed. Two datasets are used which are Cora and Pubmed. The best accuracy results in our experiment are 75.25% and it is shown when we use the Pubmed dataset. This result is the highest accuracy compared with other graph clustering models using Pubmed dataset.
Sentiment Analysis on Students' Satisfaction of the Tanauan City College using Naïve Bayes Algorithm

Sentiment analysis (SA) is a technique for determining how people feel or think about the products or services offered by a particular organization. It is the study of people's thoughts, feelings, judgments, attitudes, and emotions as expressed in written language. In Higher Education Institutions (HEIs), it is important to identify the sentiments of each stakeholder to ensure its continuous improvement. One of the stakeholders that should not be ignored and whose opinion may greatly impact the institution is the students. Their opinions may impact HEI's brand image. Tanauan City College (TCC) is one of the HEIs recognized by the Commission on Higher Education (CHED) under the category of Local Universities and Colleges (LUCs). At TCC, the students openly express their sentiments through social media platforms or even through the use of Google Forms. Mining those sentiments may be the technique of the institution to understand its stakeholders' experiences and may serve as inputs to decision-making to improve its services and the quality of education it provides. The study's main objective is to analyze the sentiments of TCC stakeholders using the Naïve Bayes (NB) algorithm. Data collection, preprocessing, categorization, validation and assessment, and NB integration are the proposed incremental processes used in the study. Findings of the study revealed that the implementation of the NB algorithm returned an accuracy of 85.41% for the sentiment analysis of TCC students. Therefore, the study confirms that NB is one of the choices for text categorization and could be an efficient and optimum solution for SA problems.
Tiled-DASH VR Video Streaming: Design and Implementation

Supporting virtual reality (VR) via cellular networks attracts increasing attention and is a promising use case in the 5G and 6G networks. to achieve this, it calls for a bandwidth-efficient video transmission protocol, where the tiled-based virtual reality video transmission with dynamic adaptive streaming over HTTP (tiled-DASH) is recommended by 3GPP. It introduces complicated system model crossing transmission and video processing subject areas. to investigate this, we build a tiled-DASH testbed with motion prediction, perform the traffic modeling, analyze the delay and bandwidth usage, and measure the performance of multiple prediction algorithms. The experimental results identify the trade-off among prediction horizontal, segment length, and bandwidth usage, which fails to be captured by literature.
Degeneration vs Classical of simulated annealing algorithm: performance analysis

An energy system is said degenerate if it corresponds to several distinct states of an atom or other quantum system. the degeneration simulated annealing algorithm (DSA) is a proposed variant of simulated annealing algorithm. The DSA is based on a process to search the optimal energy state (optimal solution), by subdividing the search system on two distinct search subsystems. In this paper, we analyze the effectiveness of the degeneration simulated annealing compared to the classical simulated annealing algorithm, by analyzing the annealing process to converge to the optimal state (solution), we use as a NP hard problem the estimate of the minimum distance value for BCH(127,64) and BCH(255,91).