Reviewerly/exHarmony huggingface dataset:
Robust Tracking Strategy for Modern GNSS Receivers in Sounding Rockets

This paper presents a robust tracking strategy for global navigation satellite system (GNSS) receivers in sounding rockets. The continuous tracking of sounding rockets using G NSS signals becomes challenging due to the high signal dynamics experienced, especially during lift-off. Therefore, robust tracking techniques are crucial to keep the lock of the satellite signals and achieve a continuous position, velocity, and time (PVT) solution of the sounding rocket. This research presents a robust adaptive tracking technique: the loop-bandwidth control algorithm (LBCA)-based frequency locked loop-assisted-phase locked loop (FAP). This technique is compared with state-of-the-art robust tracking techniques using fixed tracking configurations. Both methods are tested in a simulated rocket launch scenario. Results show that the LBCA-based FAP is a reliable option for sounding rocket scenarios due to its robustness against high dynamics.
Tiira: an Open-Source Hardware-based GNSS Receiver and Multi-sensor Navigation System

This paper introduces Tiira - an open-source hardware-based Global Navigation Satellite Systems (GNSS) receiver and multi-sensor navigation system. Its unique features include system modularity, an openness of software and hardware designs, signal processing running in dedicated soft-core proces-sors supported by an accelerator implemented in hardware logic. The work on the platform is ongoing; this paper describes the overall system, its architecture and structure, design choices and challenges, and the development status. We also provide some preliminary results.
Multipath concurrent entanglement routing in quantum networks based on virtual circuit

Quantum network is one of the important development directions of network in the future. Consideration should be given to the unique physical properties of quantum. In quantum networks with relatively limited resources (such as quantum memory), decoherence will lead to qubits loss, which restricts the time that data qubits can be stored in memory before use. Therefore, transmitting data qubits as soon as possible is very important to realize the network function. In order to ensure timeliness, this paper proposes a multipath concurrent entanglement routing scheme based on virtual circuit in quantum networks. Compared with the single path method, this scheme has obvious advantages in reducing the time required to establish the quantum entanglement channel and to transmit the measurement outcomes.
The optimization of white blood cell classification based on EfficientNet model

With the quick development of artificial intelligence (AI), it&#x2019;s common to see that AI has been used in all kinds of industries to improve the efficiency and help us human to pay more attention to other essential aspects. One of the most important applications of AI is to recognize and classify the objects in the picture. Therefore, it can also be utilized in the area of blood test. In this paper, the blood cell recognition system is constructed to identify different kinds of white blood cells. The main structure of this system is a CNN based on EfficientNet, with the optimizing of adjusting parameter like learning rate according to the change of loss and accuracy.
Calculation of sentence vector similarity based on fasttext model of weighted fusion

Traditional text similarity algorithms suffer from inadequate feature extraction, and most of them do not calculate text similarity from features in both sentence and word dimensions. To solve these problems, a sentence vector similarity calculation method based on fasttext model of weighted fusion is proposed. The method generates word vectors by fasttext model, obtains sentence vectors consisting of word vectors weighted by word frequency and word order, gets sentence-level similarity by calculating cosine similarity, and gains word-level similarity by W-WRD algorithm. The final similarity result is obtained by this two levels of sentence vector similarity. Experimental results show that the model still has high accuracy on small data sets, outperforms the model before fusion, performs well on multiple data sets, and has good transferability and stability.
Dialogues on Decolonial Participatory Design Praxis During a Revolution

On the 17th of October 2019, people from across Lebanon participated in nation-wide protests which ignited a revolution condemning the political elite and the dire socioeconomic conditions they have engendered. Such events through which people are (re)imagining their lifeworlds prompt us as Lebanese design researchers to (re)imagine Participatory Design within a decolonial and decolonizing narrative. In an effort to (re)imagine PD so that we think/do with and from the revolution, we present dialogues held amongst: ourselves, with Lebanese public health practitioners/activists and with decolonial/decolonizing literature. Through these dialogues we unsettle our entanglements and raise the contradictions and questions within PD that we are contending with. In doing so we contribute to the growing efforts to decolonize PD so that we create worlds otherwise.
A Three-Step Framework for Detecting Malicious URLs

In order to solve the shortcomings of using blacklist method to detect malicious URLs, such as slow update speed, the research of using machine learning to detect malignant URLs are increasing. These researches have proposed their own methods and obtained great accuracy, but the summary research on malicious URLs detection is insufficient. In this paper, we propose a three-step framework for malicious URLs detection and we overview 14 related works by our three-step framework and find that almost all researches of malicious URLs detection using machine learning can be classified by three-step framework. We evaluate some machine learning models and context-considering methods and their suitability by our three-step framework. According to the results, we verify the importance of considering context and find that context-considering embedding methods are more important and the malicious URLs detection accuracy improved with context-considering methods.
An Effective Chinese Text Classification Method with Contextualized Weak Supervision for Review Autograding

This paper aims to develop a Chinese text classification workflow in education situations, where a grade can swing due to subjective cognitive loads. This problem is often observed between the academic paper comments and their grades, leading to a challenge in Chinese texts. To analyze this problem, we in this paper introduce an effective Chinese text classifier by extending the popular seed words-based model into an effective workflow. We first made texts into vectors in the proposed method using Chinese preprocessing. We then exploited the bidirectional encoder representations from Transformers to integrate the contextualization features, then performed a hierarchical attention network for classification. In this study, we collected 4,310 review comment short-texts involving 140 universities in China. As these texts include noisy grades from experts, the proposed method yields seed words for each category, resulting in pseudo labels to weakly supervise the network training instead of the noisy labels. We finally evaluated the designed workflow on the real-world datasets and achieved a good performance in Chinese classification compared with the traditional models. This study provides insights into a real educational text case where a review grade can swing due to subjective cognitive loads and an available workflow to automatically grade these Chinese expert comment texts, facilitating the precise academic evaluation system.
Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms

Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time.
Efficient Color Quantization Using Superpixels

We propose three methods for the color quantization of superpixel images. Prior to the application of each method, the target image is first segmented into a finite number of superpixels by grouping the pixels that are similar in color. The color of a superpixel is given by the arithmetic mean of the colors of all constituent pixels. Following this, the superpixels are quantized using common splitting or clustering methods, such as median cut, k-means, and fuzzy c-means. In this manner, a color palette is generated while the original pixel image undergoes color mapping. The effectiveness of each proposed superpixel method is validated via experimentation using different color images. We compare the proposed methods with state-of-the-art color quantization methods. The results show significantly decreased computation time along with high quality of the quantized images. However, a multi-index evaluation process shows that the image quality is slightly worse than that obtained via pixel methods.
Deep-Learning Based Injection Attacks Detection Method for HTTP

In the context of the new era of high digitization and informatization, the emergence of the internet and artificial intelligence technologies has profoundly changed people’s lifestyles. The traditional cyber attack detection has become increasingly weak in the context of the increasingly complex network environment in the new era, and deep learning technology has begun to play a significant role in the field of network security. There are many kinds of attacks against web applications, which are very harmful, including SQL (Structured Query Language) injection, XSS (Cross-Site Scripting), and command injection. Based on the detection of SQL injection and XSS attacks, this paper combines the detection of command injection attacks, which are also very harmful, and proposes a multi-classification detection method for web injection attacks. We extract features in the URL (Uniform Resource Locator) and request body of HTTP (Hyper Text Transfer Protocol) requests and combine deep learning technology to build a multi-classification model for injection attacks. Firstly, aiming at the problem of imbalanced distribution of training samples and low detection accuracy of command injection attack, a sample generation method is proposed. The experimental results show that the proposed method ensures a higher detection rate of command injection attacks and lower false alarms. Secondly, we propose a more expressive feature fusion model, which effectively combines the features extracted by deep learning with the discrete features extracted manually. The experimental results show that the feature fusion model proposed in this work is more effective compared with a single deep learning model. The accuracy of the model is improved by about 1%.
The Relationship Between the Personality Type 0f Teachers and Methods of Stimulating Their Professional Activities

Encouraging employees to the most productive work and to striving to continue to engage in their activities is one of the most important tasks of human resource management services at the present time. This is especially true for the work of teachers, because the desire to transfer their knowledge, constant readiness for self – development and enthusiasm are the main keys to the implementation of an effective educational process. &#x0D; There are several definitions of the concepts of "motivation" and "stimulation" in the theoretical part of the article, that are given by different authors and a comparison of the usage of these concepts. The main aspects of the methodology of J. Holland, used further in the article. &#x0D; The practical part of the article presents the results of an empirical study of the personality types of teaching staff and the relationship of the types obtained with the ways to stimulate their labor activity.&#x0D; There are recommendations at the end of the article, that are given to improve the motivation of teachers by stimulating them in various ways, depending on their personality type, and also there are some conclusions.
Cooperative Bayesian optimization with hybrid grouping strategy and sample transfer for expensive large-scale black-box problems

Expensive large-scale optimization problems are difficult to solve owing to their high complexity and computational costs. Bayesian optimization is an effective method for dealing with low-dimensional, costly problems. This study extends Bayesian optimization to expensive large-scale optimization problems. In the study, the large-scale problem is decomposed into a series of low-dimensional subproblems using the proposed hybrid grouping strategy. Each subproblem is chosen based on its contribution to the improvement of the best overall objective value, and then Bayesian optimization is used to solve it. This study proposes a sample transfer strategy and a novel transfer Gaussian process regression model to improve the efficiency of Bayesian optimization. The sample transfer strategy can take advantage of the additive separability of the objective and thus transfers the historical samples by adding a constant to their objective values. The transfer Gaussian process regression model can employ the historical samples by reducing their correlations according to the differences in their parameters (i.e., variables other than those of the current subproblem). The proposed algorithm is tested on 15 large-scale benchmark problems up to 1000 dimensions. The experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms, and the proposed hybrid grouping strategy, sample transfer strategy, and transfer Gaussian process regression improve the efficiency of the cooperative Bayesian optimization framework even further.
Virtual Intelligence in the Post-Pandemic Era

Numerous yet deceptively subtle differences between virtual and face-to-face communication profoundly influence organizational performance, team interaction, and psychological well-being. This chapter identifies the “Big 10” factors that affect human communication in virtual mediums: (1) mutual gaze, (2) directional gaze, (3) objective self-awareness, (4) seating configurations, (5) back-channel utterances, (6) side conversations, (7) touching, (8) gesturing, (9) collisions (unplanned face to face encounters between colleagues), and (10) shared context. This chapter then examines research on each of these key factors, discusses how each affects virtual communication, and suggests best practices for overcoming and navigating these challenges.
Isolated forest-based ZigBee Device Identification Using Adaptive Filter Coefficients

With the widespread use of wireless network technology, more and more attention has been paid to the Internet of things (IoT) device security. The radio frequency fingerprint-based device identification technology can effectively improve the security of wireless devices. In this paper, a novel RF fingerprint extraction method employing least mean square (LMS) filter coefficients is proposed. The work involved in this paper includes two parts: feature extraction and feature identification. The feature extraction stage includes signal synchronization, frequency offset estimation, phase offset estimation, and adaptive filtering parameter extraction. In the feature identification stage, random forest (RF) is used to verify the distinction of this fingerprint, and isolated forest (IF) is used to identify the fingerprint from unknown devices. Our experiment is conducted on 54 ZigBee devices. The experimental results show that the recognition accuracy of legal equipment and illegal equipment is 100% and 89.83% respectively in a 10dB signal-noise ratio (SNR) environment, and 98.15% and 99.55% respectively in a 30dB SNR environment.
Digital Signature Performance of a New Quantum Safe Multivariate Polynomial Public Key Algorithm

We discuss the performance of a new quantumsafe multivariate digital signature scheme proposed recently, called the Multivariate Polynomial Public Key Digital Signature (MPPK DS) scheme. Leveraging MPPK KEM or key exchange mechanism, the MPPK DS scheme is established using modular exponentiation with a randomly chosen secret base from a prime field. The security of the MPPK DS algorithm largely benefits from a generalized safe prime associated with the said field and the Euler totient function. We can achieve NIST security levels I, III, and V over a 64-bit prime field, with relatively small public key sizes of 128 bytes, 192 bytes, and 256 bytes for security levels I, III, and V, respectively. The signature sizes are 80 bytes for level I, 120 bytes for level III, and 160 bytes for level V. The MPPK DS scheme offers probabilistic procedures for signing and verification. That is, for each given signing message, a signer can randomly pick a base integer to be used for modular exponentiation with a private key, and a verifier can verify the signature with the digital message, based on the verification relationship, using any randomly selected noise variables. The verification process can be repeated as many times as the verifier wishes for different noise values, however, for a true honest signature, the verification will always pass. This probabilistic feature largely restricts an adversary to perform spoofing attacks. In this paper, we conduct some performance analyses by implementing MPPK DS in Java. We compare its performance with benchmark performances of NIST PQC Round 3 finalists: Rainbow, Dilithium, and Falcon. Overall, the MPPK DS scheme demonstrates equivalent or better performance, and much smaller public key, as well as signature sizes, compared to the three NIST PQC Round 3 finalists.
Super-resolution Reconstruction of Night-light Images Based on Improved SRCNN

Night-light remote sensing images have been used in numerous fields and have brought much value to human society. However, the low resolution is a major drawback of night-light images, limiting its application in many ways. Image processing research proposes super-resolution reconstruction technology, which can obtain high-resolution images from low-resolution images. It is often used in some fields that need image details. We improve the SRCNN network structure by removing the bicubic interpolation with high computational complexity and replacing it with a deconvolutional layer. We added a feature extraction layer. We can extract more features in the image by using two convolution layers to extract the enlarged image. Finally, we also optimize the convolutional layer of the nonlinear mapping to reduce the training time. We produced a training set and a test set of night light images to evaluate the effect of network improvement. Through the final comparison, our method can improve the resolution of luminous image, which is greatly improved compared with SRCNN.
Security Enabled Framework to Access Information in Cloud Environment

This paper proposed a Security Enabled Framework to Access the Information in Cloud Environment (SEFAI) which is designed by using the various secure encrypted algorithms to provide information security while accessing information from the cloud. There are several security issues that have emerged while service delivery models are used in the cloud system. With the advancements in cloud computing day by day different cloud based services are used by cloud users like AWS (Amazon Web Services), Microsoft Azure, Google etc. The security issues are very important during the time of accessing information from cloud. To resolve the security issue a novel framework is designed by using cryptography algorithms. The designed framework takes less encryption and decryption time in comparison of existing framework that was “Security Algorithms for Cloud Computing (SACC)” [1]. The proposed framework in this paper provides more security to the data in cloud environment and prevent the unauthorized users to access the information.
The rise of mobile development: a comparison between Ionic and Flutter

A dive into the world of mobile development frameworks. An overview regarding the mobile technologies, in general but also a close look at two major mobile development frameworks, which are Ionic and Flutter. They are being analyzed at an individual level and furthermore, a comparison between these two technologies is taking place.
Cryptographic and Financial Fairness

A recent trend in multi-party computation is to achieve cryptographic fairness via monetary penalties, i.e. each honest player either obtains the output or receives a compensation in the form of a cryptocurrency. We pioneer another type of fairness, financial fairness, that is closer to the real-world valuation of financial transactions. Intuitively, a penalty protocol is financially fair if the net present cost of participation (the total value of cash inflows less cash outflows, weighted by the relative discount rate) is the same for all honest participants, even when some parties cheat. We formally define the notion, show several impossibility results based on game theory, and analyze the practical effects of (lack of) financial fairness if one was to run the protocols for real on Bitcoin using Bloomberg’s dark pool trading. For example, we show that the ladder protocol (CRYPTO’14), and its variants (CCS’15 and CCS’16), fail to achieve financial fairness both in theory and in practice, while the penalty protocols of Kumaresan and Bentov (CCS’14) and Baum, David and Dowsley (FC’20) are financially fair.