Reviewerly/exHarmony huggingface dataset:
The complexities of participatory action research: a community development project in Bangladesh

In this chapter, we examine a complex PAR project about mobile phones and remote village women that took place in Bangladesh via a major International NGO (Oxfam), but was developed and managed with international university partners. Consequently, we came to the project from very different perspectives, cultures, and educational backgrounds, including that of Western university academics, local project staff in an international NGO, local staff in a remote NGO, and the villagers themselves in a hierarchical, gendered, and traditional environment. Lack of access to villages involved in the project and communication problems also affected the implementation of PAR. While we had hoped for a project that was strongly driven from the bottom, what resulted was more modest. Village women demonstrated changes in skills and attitudes towards mobile communications, but changes in social relations and information production were much more modest. As foreigners, we were greatly constrained in what we could do to change traditional community attitudes. Despite such constraints, the idea of PAR has influenced Oxfam's thinking about its work, including how it uses information technology for community development. The project also taught us much, as foreigners, about the sensitivities of working in a culture with its own ways of doing and being, as well as the difficulties of matching project desires to the real challenges of environmental disasters or political emergencies such as a refugee crisis.
Determining Hybrid Re-id Features of Vehicles in Videos for Transport Analysis

The research topic presented in this paper belongs to computer vision problems in the transport application area, where the statistical data of the results give the input for the transport analysis. Although object tracking in a controlled environment could be performed with good results in general, accurate and detailed annotation of vehicles is a common problem in traffic analysis. Such annotation includes static and dynamic attributes of numerous vehicles. Most recent object trackers employ CNNs to compute the so-called re-identification features of the bounding boxes. In this paper we introduce hybrid re-identification features, which combine latent, static, and dynamic attributes to improve tracking. Furthermore, we propose a lightweight solution that could be integgrated in a real-time multi-camera tracking system.
RoamFab: A Design Tool for Reconfiguring Parameterized Mechanisms to 3D Models With Structural Optimization

Creating personalized 3D printing objects has become increasingly popular with the advance in end-user modeling tools and fabrication techniques. However, it remains challenging for novice makers to design and fabricate functional objects with mechanical motion such as linear or rotational. To empower users to add mechanical movements to their models for particular needs, we first investigate the commonly used 3D printable mechanisms and parameterize four mechanical primitives. We then provide a computational approach to reconfigure the mechanical primitives to user-provided 3D models, with an underlying structural optimization procedure according to a specified bending force. We further showcase a set of design examples and conduct a user study to demonstrate the potential of our approach in creating personalized functional artifacts.
Usability estimation of component-based software system using adaptive neuro fuzzy approach

Cost effective development is the prime goal for software developers. To achieve this goal, nowadays, component-based software system (CBSS) are developed. In CBSS, the existing components are reused to develop a new software system which increases the reusability of components. It also reduces time and efforts of software developers, which is cost effective. The success or failure of software system depends on its usability. Usability can increase the market revenue. So, to increase the acceptance rate of CBSS among the users, it is important to evaluate the usability of CBSS before the software is released. In this paper, usability of CBSS is evaluated based on four input factors by using two widely used soft computing techniques, i.e., fuzzy logic and adaptive neuro fuzzy logic. Experimental results obtained from both the techniques are also compared and it is observed that ANFIS approach reduces the error rate and provide more accurate results. This research work will help the software developers to estimate the usability of CBSS in a more efficient manner.
Time Series Data Prediction and Feature Analysis of Sports Dance Movements Based on Machine Learning

Sports dance is a competition project and a kind of sports, with the characteristics of being smooth, generous, leisurely, and comfortable, dance steps, smooth movements, and flowing clouds, and it can give full play to the indoor space. In the light of the new era, sports dance is also playing an increasingly important role. Through the time series data and feature analysis of dance sports movements through machine learning, the internal information is mined to find the trends and laws. Machine learning in the era of big data is widely used in research as the main tool for data analysis and mining. The key difficulty of data mining has always been time series data. Machine learning refers to a method of using the resulting data in a computer to derive a certain model and then using this model to make predictions. The core is “using algorithms to parse data, learn from it, and then make decisions or predictions about new data.”
Covert VoIP Communication based on Audio Steganography

Voice over Internet Protocol (VoIP) is a popular and important internet protocol for real-time voice calling.It is used in several software applications such as Skype, WhatsApp, and Google Talk.However, communications over the internet can easily be exposed to hacking and eavesdropping.In secret communication, a private channel with robust encryption can provide the required protection for the communications.But private channels have more constraints than the public channel, such as specific communication devices and specific regions to cover.On the other hand, public communications such as VoIP can be done from anywhere without extra costs.This paper proposes a real-time covert communication approach that generates a secret speech channel inside a public speech channel based on audio steganography.The process requires high embedding capacity in a real-time manner.Therefore, the proposed approach encodes the secret speech using Internet Low Bit Rate Codec (iLBC) that can be used as a VoIP source Codec to compress the secret input speech with auto-correction for speech quality degradation over a lossy channel.Also, the cover speech is compressed by G.711 encoder at synchronous time periods (for each 20 ms) with respect to the secret speech to meet the requirements of the VoIP.Subsequently, the secret data hides in compressed cover speech data in real-time.The embedding procedure employs the only high strength data to maintain the quality of the cover data after embedding at greater than 40 dB in terms of signal-to-noise ratio (S NR).The secret data is 100% retrieved without error in the case of a lossless channel.This approach can be used in secure communications such as military and government communications.
Machine Learning Approaches for Whisper to Normal Speech Conversion

Whispered speech is a mode of speech that differs from normal speech due to the absence of a periodic component, namely the Fundamental Frequency that characterizes the pitch, among other spectral and temporal differences. Much attention has been given in recent years to the application of Machine Learning techniques for voice conversion tasks. The whisper-to-normal speech conversion is particularly challenging, however, especially with respect to the Fundamental Frequency estimation. Based on the most recent literature, this survey assesses the state-of-the-art regarding Machine Learning based whisper-to-normal speech conversion, identifying trends both on modeling and training approaches. The proposed solutions include Generative Adversarial Network based, Autoencoder based and Bidirectional Long Short-Term Memory based frameworks, among other Deep Neural Network based architectures. In addition to Parallel versus Non-Parallel training, time-alignment requirements and strategies, datasets, vocoder usage, as well as both objective and subjective evaluation metrics are also covered by the present survey.
Reinforcement Learning

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Gaussian trajectory PMBM filter with nonlinear measurements based on posterior linearisation

This paper presents a Gaussian implementation of the Poisson multi-Bernoulli mixture (PMBM) filter for sets of trajectories with non-linear/non-Gaussian measurements. In this filter, the single-trajectory densities are Gaussian and their updates are performed using the iterated posterior linearisation technique, applied on the current target state. With this approach, we first compute the posterior distribution of the current target state by iteratively refining the linear approximation of the measurement, and the resulting mean square error of the linearisation, based on our current guess of the posterior distribution. After obtaining a Gaussian approximation of the current target state, the distribution of the past states of the trajectory can be obtained in closed form. Via numerical simulations, we compare different algorithms to approximate the single-trajectory posteriors and normalising constants for trajectory PMBM and trajectory Poisson multi-Bernoulli filters.
The Development of e-Book Based on Problem and Project Based Learning Assisted by Book Creators

The aim of this research is; 1) develop an initial e-book assisted PPjBL approach platform Book Creator for fourth grade elementary school students; 2) describes the initial draft form of teaching materials in the form of an e-book based on the PPjBL approach assisted by a platform Book Creator for fourth grade elementary school students. This research is a kind of research and development or R&amp;D (Research &amp; Development). This study uses the Sukmadinata modification research stage which consists of 3 stages, such as; preliminary study (stage 1) consists of library research activities, field observations, and preparation of an initial draft; development (stage 2) consists of the product preparation stage, design validation, improvement, product testing, product improvement and determination; and product testing and socialization (stage 3). Data was collected using test and non-test techniques in the form of observation. This study uses qualitative data analysis techniques. Based on the results of the study, it was concluded that the e-book developed was "Very Appropriate" with a percentage of 81%. Moreover, based on the results of the product trials at a 95% confidence percentage obtained that the average value of the students after using the e-book was equal to 80.
Rat Swarm Optimizer for Data Clustering

Rat Swarm Optimization (RSO) is one of the newest swarm intelligence optimization algorithms that is inspired from the behaviors of chasing and fighting of rats in nature. In this paper we will apply the RSO to one of the most challenging problems, which is data clustering. The search capability of RSO is used here to find the best clusters centers. The proposed algorithm RSO for clustering (RSOC) is tested on several benchmarks and compared to some other optimization algorithms for data clustering including some wellknown and powerful algorithms such as Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and other recent such as the Hybridization of Krill Herd Algorithm and harmony search (H-KHA), hybrid Harris Hawks Optimization with differential evolution (H-HHO), and Multi-Verse Optimizer (MVO). Results are validated through a bunch of measures: homogeneity, completeness, v-measure, purity, and error rate. The computational results are encouraging, Where they demonstrate the effectiveness of RSOC over other techniques.
PRIVAFRAME: A Frame-Based Knowledge Graph for Sensitive Personal Data

The pervasiveness of dialogue systems and virtual conversation applications raises an important theme: the potential of sharing sensitive information, and the consequent need for protection. To guarantee the subject’s right to privacy, and avoid the leakage of private content, it is important to treat sensitive information. However, any treatment requires firstly to identify sensitive text, and appropriate techniques to do it automatically. The Sensitive Information Detection (SID) task has been explored in the literature in different domains and languages, but there is no common benchmark. Current approaches are mostly based on artificial neural networks (ANN) or transformers based on them. Our research focuses on identifying categories of personal data in informal English sentences, by adopting a new logical-symbolic approach, and eventually hybridising it with ANN models. We present a frame-based knowledge graph built for personal data categories defined in the Data Privacy Vocabulary (DPV). The knowledge graph is designed through the logical composition of already existing frames, and has been evaluated as background knowledge for a SID system against a labeled sensitive information dataset. The accuracy of PRIVAFRAME reached 78%. By comparison, a transformer-based model achieved 12% lower performance on the same dataset. The top-down logical-symbolic frame-based model allows a granular analysis, and does not require a training dataset. These advantages lead us to use it as a layer in a hybrid model, where the logical SID is combined with an ANNs SID tested in a previous study by the authors.
Iterative quantum optimization with an adaptive problem Hamiltonian for the shortest vector problem

Quantum optimization algorithms hold the promise of solving classically hard, discrete optimization problems in practice. The requirement of encoding such problems in a Hamiltonian realized with a finite (and currently small) number of qubits, however, poses the risk of finding only the optimum within the restricted space supported by this Hamiltonian. We describe an iterative algorithm in which a solution obtained with such a restricted problem Hamiltonian is used to define a new problem Hamiltonian that is better suited than the previous one. In numerical examples of the shortest vector problem, we show that the algorithm with a sequence of improved problem Hamiltonians converges to the desired solution.
Reality Analagous Synthetic Dataset Generation with Daylight Variance for Deep Learning Classification

For the implementation of Autonomously navigating Unmanned Air Vehicles (UAV) in the real world, it must be shown that safe navigation is possible in all real-world scenarios. In the case of UAVs powered by Deep Learning algorithms, this is a difficult task to achieve, as the weak point of any trained network is the reduction in predictive capacity when presented with unfamiliar input data. It is possible to train for more use cases, however more data is required for this, requiring time and manpower to acquire. In this work, a potential solution to the manpower issues of exponentially scaling dataset size and complexity is presented, through the generation of artificial image datasets that are based off of a 3D scanned recreation of a physical space and populated with 3D scanned objects of a specific class. This simulation is then used to generate image samples that iterates temporally resulting in a slice-able dataset that contains time varied components of the same class.
A Novel YOLOv5-based Anomalous Object Detection Algorithm in Buses

In this work, a novel abnormal objects analyzing and detecting (AOAD) algorithm inside the bus is proposed, and the AOAD algorithm is further applied to the practical bus by designing an embedded video analysis system. The proposed algorithm is based on the deep learning YOLOv5 (You Only Look Once) algorithm, which has better timeliness and accuracy in detecting anomalous objects. The anomalous objects with larger boxes are detected by building an anomalous object dataset for training. The effectiveness and applicability of the proposed algorithm is verified by extensive experiments on video data based on real buses.
PAR: Improving Video Bitrate Adaptation via Payload-Aware Throughput Prediction

Adaptive bitrate (ABR) algorithm is deployed extensively in commercial video delivery platforms, aiming to ensure users' quality of experience(QoE). Among the majority of existing ABR algorithms, throughput prediction plays a critical role. However, these predictors suffer from neglecting the throughput inconsistency across diverse chunk payloads under the network dynamics, e.g., the actual throughput of downloading a 4K or a 720P chunk is usually different, even when starting from the same moment. In this paper, we propose a payload-aware adaptive algorithm called PAR, which predicts multiple throughput estimations for different target payloads, and utilizes them to make better bitrate adaptation decisions. Trace-driven experiments show that PAR outperforms the existing ABR schemes across diverse network conditions, with the average QoE improvement of 2.66% to 79.43%.
PassTCN-PPLL: A Password Guessing Model Based on Probability Label Learning and Temporal Convolutional Neural Network

The frequent incidents of password leakage have increased people’s attention and research on password security. Password guessing is an essential part of password cracking and password security research. The progression of deep learning technology provides a promising way to improve the efficiency of password guessing. However, the mainstream models proposed for password guessing, such as RNN (or other variants, such as LSTM, GRU), GAN and VAE still face some problems, such as the low efficiency and high repetition rate of the generated passwords. In this paper, we propose a password-guessing model based on the temporal convolutional neural network (PassTCN). To further improve the performance of the generated passwords, we propose a novel password probability label-learning method, which reconstructs labels based on the password probability distribution of the training set and deduplicates the training set when training. Experiments on the RockYou dataset showed that, when generating 108 passwords, the coverage rate of PassTCN with password probability label learning (PassTCN-PPLL) reached 12.6%, which is 87.2%, 72.6% and 42.9% higher than PassGAN (a password-guessing model based on GAN), VAEPass (a password-guessing model based on VAE) and FLA (a password-guessing model based on LSTM), respectively. The repetition rate of our model is 25.9%, which is 45.1%, 31.7% and 17.4% lower than that of PassGAN, VAEPass and FLA, respectively. The results confirm that our approach not only improves the coverage rate but also reduces the repetition rate.
SM2-based low-cost and efficient parallel modular multiplication

Elliptic curve encryption (ECC) has been widely used in public key cryptography , and modular multiplication is one of the core operations of elliptic curve encryption. This paper presents a low-cost high-speed parallel modular multiplication implementation based on SM2. Using the characteristics of the prime ( P 256 ), the two-step multiplication and reduction of modular multiplication are performed in parallel. The 8-part karatsuba algorithm is used in multiplication. In the process of performing multiplication, in order to reduce the consumption of multiplier resources, the karatsuba algorithm is used to improve the ordinary multiplier. The control signal E N = 0 / 1 is used to control the multiplier to perform ordinary multiplication or karatsuba algorithm multiplication. Then the multiplier is reused. 4 improved 32-bit multipliers are used in complete 256-bit modular multiplication. Experiments show that on the 100 MHz Artix-7 FPGA hardware platform, only 12K LUTs are needed, and a modular multiplication operation can be completed in 0 . 09 μ s . Comprehensive time and area, our design has certain advantages.
Digitalization of Educational Services with Regard to Policy for Information Security

Faced with the widespread digitalization in educational services, every educational organization has to strive to meet modern-day quality and security standards for both information and other assets. The education organizations should invest in a methodology that identifies system flaws and, as a result, opportunities for improvement. To protect the educational organization's information assets from internal, external, intentional, or unintentional threats, an "Information Security Policy" should be developed. Effective data management necessitates not only the identification of data requirements but also security issues. Educators and students should be subject to policies that define and impose conditions in a variety of sensitive areas.
Review of BIM-Based Software in Architectural Design: Graphisoft Archicad VS Autodesk Revit

Revolution Industry 4.0 delivers impact on world development AEC (architect, engineering, and construction), especially in the development of useful digital technology for acceleration development infrastructure namely BIM ( Building Information Modeling ). BIM uses 3D software to support all potential and productivity in designing building construction. This article aims to review the advantages and limitations of the software Graphisoft Archicad and Autodesk Revit, especially in the field of BIM. the method used in writing an article is a review paper with a review of six journals and three expert opinions. The results of the review show that the Archicad software dominates the criteria for the field of work and 3D display. Temporary software Revit dominates in the field of work criteria in terms of the number of owned features because integrated by the developers of the Autodesk program. Archicad software is more compatible for users who want to have a more concise and easy-to-understand interface, while Revit software is more compatible for users with more complex project scales. Both of these software support parametric design. In Archicad and Revit Rhinoceros-grasshopper software can be used for Live Connection, but Archicad also has Param-O parametric object library and Revit has Dynamo for visual programming of parameter modeling. This shows that users should know more about their needs before choosing to use one of this BIM software.