Reviewerly/exHarmony huggingface dataset:
A Study on The Effectiveness of StaySafe PH in Reducing the Spread of COVID-19 in Barangay Bagong Silangan, Quezon City

The growing number of cases of COVID-19 has prompted the government to develop and deploy digital contact tracing applications. [1] The dominant explanation of these measures is mitigating the transmission of the novel coronavirus (COVID-19). [2] Contact tracing is crucial in dealing with a pandemic because it allows rapid detection of cases based on information gathered from affected individuals about other people they may have had recent contact with. Advances in digital technologies have made it possible to use cellular phones in the contact tracing process. The previous study has proved the effectiveness of the contact tracing application in various countries like Singapore and United Kingdom. Having said this, the objective of this study is to determine the efficacy of StaySafe PH in reducing the ballooning cases of COVID-19 in Barangay Bagong Silangan, Quezon City. StaySafe PH is a contact tracing application with medical consultation and allows users to report problems regarding their health conditions easily. The respondents were composed of 55 (fifty-five) residents of Barangay Bagong Silangan, whose ages are within the bracket of 18 to 59 years old. To gather the necessary data needed, the proponents created and administered a questionnaire to the subjects, which focused on the respondents' experience in using the StaySafe PH. The design used by the researcher is correlational, which gathers quantifiable information that can be used for statistical interference through data analysis. In addition, the researchers applied a probability sampling technique in this study. In findings and analysis of the data, most of the respondents are using StaySafe PH, which is thirty-six (36) percent at the same time believed that the application could help in flattening the curve and saving more lives. Furthermore, it is decided that StaySafe PH is effective and safe to use.
A Beta Distribution Based Optimization Algorithm and Its Application in Power Load Forecasting

Population-based evolution algorithms imitate lots of swarm-based behavior of creatures and even natural phenomena. In the entire evolutionary process, randomness and diversity make solution set convergence to the global optima quickly, and at the same time, make them jump out local optimums. However, this search mode based on the solution pool needs much memory space to store real solutions. The blindness of search will inevitably increase the time-consuming. Solving these disadvantages has been a hot topic in research, which is the goal of our work. In this paper, we propose a novel optimization algorithm based on Beta distribution. A known probability distribution, simulated by Beta distribution, transforms the solution set into a sampling sample. The specimen keeps updating toward the direction we expect, according to the characteristics of the probability distribution. This model reduces the memory space occupied by candidate solutions and also reduces the complexity of the algorithm. Finally, we analyzed and verified the performance of the proposed algorithm through numerical experiments, especially when dealing with large-dimensional problems. And its performance at power load forecasting has also proved its advantages in handling big data.
Categorizing Chinese Glyphs Based on Liushu With Convolutional Neural Networks

As one of the most polupar languages in the world and the only live language of ideograph, Chinese can be learned and mastered fast and easily if the learners have the knowledge of Chinese glyphs evolution, categorization and creation detailed in Liushu — the earliest systematic theory on the structure of Chinese characters. However, there is no valid and accurate way from machine learning to classify Chinese glyphs based on Liushu. In this paper, we used Convolutional Neural Networks to recognize and classify images of Chinese glyphs based on Liushu and achieved 94% accuracy in the Duti/Heyi binary classification, which can be used for classifying unclassified Chinese glyphs and assisting Chinese language education.
Project-Based Learning Focused on Professional Skills: An Approach Applied on Human-Computer Interaction and Software Requirements Under-Graduation Courses

Human-Computer Interaction and Software Requirements are courses that demand special attention to concepts. Precisely, and because they are more theoretical, it becomes difficult to motivate undergraduate students to acquire professional skills that are desired in the market. The idea is to bring them closer to the new profile of the Software Engineer. In this profile, skills such as proactive behaviour, critical sense and harmony in collective work are expected. Based on this context, educational approaches that allow the development of these skills become necessary. This paper presents an experience report on the use of Project-based Learning in the Software Requirements and Human-Computer Interaction courses, during two terms. The application was conducted with Participatory Action Research, accompanied by data collection using questionnaires, and qualitative analysis of these data, from the perspective of undergraduate Software Engineering instructors and students from the University of Brasilia. The experience report described in this paper addresses the use of Project-based Learning in subjects, in which the topics taught are on a higher level of abstraction (in other words, more theoretical and conceptual). Theses particularities differ our contributions from other analyzed proposals, in which there is a more practical and applied profile. The obtained results are promising and indicate that Project-based Learning improves professional skills, allowing a review on the traditional-based learning methods commonly used in courses with similar profiles. As further work, we intend to adjust some perceived weaknesses, such as: dealing more adequately with social aspects.
Double-Stream Segmentation Network with Temporal Self-attention for Deepfake Video Detection

As deep learning technology develops rapidly, the adverse impact of counterfeiting with deep learning is expanding. It has become a hot research topic to explore ways of detecting deepfakes, i.e., fake images or videos generated with deep learning methods. The results reported in the literature show that many existing detection methods deteriorate severely in cross-dataset tests. To solve this problem, we propose a novel network that embodies three key features: 1) double-stream feature extraction module to reveal artifacts simultaneously from the original image space and the noise residual space; 2) temporal self-attention module to infer the authenticity of the current frame from the temporal context; 3) tampering region segmentation module that is based on fully convolutional network and predicts the tampered face area in each frame. We also propose a modified IoU (Intersection-over-Union) measurement of the predicted tampering region against the face region, upon which the authenticity of a frame is determined. We conduct comprehensive comparative experiments on five major datasets and demonstrate the superiority of our proposed model, particularly in the challenging cross-dataset settings.
Design and Research of UA V Intelligent Anti-crash Device

With the continuous development of the times, the application of UAV has become more frequent, and consumers of UA V have put forward higher requirements for UA V and airborne equipment. In the actual use of equipment, accidents such as unmanned aerial vehicle parking, stalling and crashing in the air due to operational errors or failure of the airborne flight control system occur from time to time. In order to reduce the adverse effects of crashes, this paper designs a independent work, independent decision-making, repeated use of UAV crash prevention device, and elaborated the theoretical support of the device, the design idea of the device's mechanical structure, the design idea of the autonomous decision algorithm, and the corresponding control circuit based on the parachute excitation device. And complete related test experiments. In this paper, by simulating the action test of the UAV, the UAV anti-crash device autonomously opens the parachute to meet the expected design requirements, which verifies the feasibility of the design and research content of this paper.
Compressing large sample data for discriminant analysis

Large-sample data became prevalent as data acquisition became cheaper and easier. While a large sample size has theoretical advantages for many statistical methods, it presents computational challenges. Sketching, or compression, is a well-studied approach to address these issues in regression settings, but considerably less is known about its performance in classification settings. Here we consider the computational issues due to large sample size within the discriminant analysis framework. We propose a new compression approach for reducing the number of training samples for linear and quadratic discriminant analysis, in contrast to existing compression methods which focus on reducing the number of features. We support our approach with a theoretical bound on the misclassification error rate compared to the Bayes classifier. Empirical studies confirm the significant computational gains of the proposed method and its superior predictive ability compared to random sub-sampling.
Construction and Analysis of Tibetan AMDO Dialect Speech Dataset for Speech Synthesis

In recent years, with the development of deep learning, speech synthesis technology has developed rapidly. Limited to the small size of corpus data, the field of Tibetan speech synthesis has several problems, such as slow development and insufficient in-depth research. Therefore, the construction of Tibetan speech synthetic dataset is of great significance. In this paper, to meet the needs of speech synthesis system based on neural network, according to the methods of phoneme balance, Tibetan character frequency analysis, Tibetan character word-formation analysis, we design a speech corpus which abides by the characteristics of Tibetan Amdo dialect. Using professional instruments and equipment, we record two professional hosts Tibetan Amdo audio, and make a 60 hours of Tibetan Amdo dialect speech dataset. In the mainstream end-to-end speech synthesis framework, it can prove that the synthetic speech is natural and understandable, which shows the effectiveness and usefulness of our dataset.
Privacy Preserving Authentication and Access Control Scheme in Data Markets

Online company for various fields now has high margins due to its more transparent and complex nature. Several data holders’ data can be shared with third-party vendors when conducting online business, and those third-party vendors can then share it with end users through those data vendors. If the data owner wishes to sell any digital content over online sources when processing these things, it is presently very difficult to keep track of data protection, privacy, and honesty while doing it. In this paper, privacy preserving and authentication and access control scheme in data markets are implemented. In this, online database performs the revocation and registration process. First, user should register with raw data. The raw data will be authenticated using authenticated data encryption technique. Next, this encrypted data will be processed using data processing. Pseudo-identity generation will provide the password for driver authentication. After data is encrypted, it is verified. This is verified based on two batches. First batch will be used for verification, and second batch will collect the encrypted data. At last, online data will be secured very effectively.
Programming Politics

Abstract Chapter 5 outlines the community of developers who have contributed to Bitcoin’s source code. Drawing from ethnographic data and existing political economy theorisations of cryptocurrencies, the governance of the Bitcoin codebase is understood through obligatory passage points found among key individuals and groups involved in the creation of Bitcoin. The consensus model for making changes to the Bitcoin software shows how code is bound up with political tensions that arise through coordinating geographies of production. Pressures between different stakeholders are exposed to show how a stagnation of decision-making in code development and the increased likeliness of the project forking as it scales demands degrees of centralisation at the architectural level of cryptocurrency design so actions can be resolved and implemented. The overall political framework for altering Bitcoin’s code is described as senatorial governance: a (de)centralised model where bureaucratic parties compete to change the monetary policy (codified rules) of the protocol.
A Multi-feature Emotion Classification Model Based on LDA Subject Target Words

In recent years, a variety of sentiment classification models based on deep neural networks have emerged. Most of the existing models are trained based on word embedding, or rely on expensive word-level annotation, or use sentence-level annotation only. However, some important linguistic phenomena and resources have not been fully studied. Aiming at the linguistic phenomenon that a sentence may have multiple sentiments and different target words may have different sentiments, this thesis proposes a multi-feature sentiments classification model based on LDA. The model automatically extracts the subject target words through LDA, screens the global sentiment features of sentences, extracts the local sentiment features of sentences with the external sentiment vocabulary, and integrates various features with the sentiments classification model. A series of experiments on three datasets show that the multi-feature model is effective. The introduction of LDA can not only reduce the demand for labeled target words, improve the accuracy of sentiments classification, but also more accurately analyze the internal emotional trend of public opinion events.
Object Re-Identification with Synthetic Training Data in Industrial Environments

Traditional object detection methods require large amounts of training data. Eliminating the need to generate this data saves time and manual or computational effort. In the field of object re-identification (Re-ID) the similarity of objects is defined by comparing query and target images. Given a query image from an object, these algorithms are able to retrieve the object in a target image, even if the class of the object is not known to the network. In industrial environments, CAD data usually exists for mechanical components. We present a system that uses this CAD data to generate query images to retrieve mechanical components in real images. We train our system exclusively with synthetic training data and evaluate the reality gap when testing real target images. In the evaluation we show, that our system is also able to retrieve unknown classes it has not been trained with. Finally, we identify improvements of our system and address potential research directions of object Re-ID in the future.
Attention-guided Soft Ranking Loss for Resource-constrained Head Pose Estimation

This paper presents a novel model for head-pose estimation from a single image with a compact model size. Previous state-of-the-art methods often rely on large training models and converge slowly on standard GPUs. In this paper, we introduce attention-guided soft ranking loss that reduces the size of the state-of-the-art method while increasing its performance. Specifically, we design an attention module to encourage learning on salient features. In addition, we propose a pair-wise soft ranking loss that supervises the model with paired samples and penalizes incorrect ordering of head-pose prediction. Considering the lack of large-pose data, we also introduce a minority head-pose oversampling algorithm to balance the distribution of yaw, pitch, and roll angles. Experiments on BIWI and AFLW2000 datasets demonstrate that our approach significantly outperforms the state-of-the-art methods. Extensive ablation studies further validate the effectiveness and robustness of the design of our framework. Code will be made available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sup> .
An Energy Balance Model Parameter Estimation with an Extended Kalman Filter

This paper presents modeling and parameter estimation of a thermal system, which is often nonlinear, with nonlinearities found in its parameters. As a result, it requires an additional online parameter estimation. In this paper, we implement an Extended Kalman Filter for modeling and parameter identification of such a device. The thermal device that we use is an educational device developed in-house. First, we derive the device’s mathematical model using a zero-order energy balance model as the template model. Next, we find the model’s best parameters by performing an optimization process. Finally, we implement an Extended Kalman Filter technique to accommodate the possibility that those parameters may change over time. Our experiments show that when we have reliable measurements and a reliable system model, the Kalman filtering technique can perform well as an online parameter estimator and act as a basis to build an adaptive model.
Differential analysis of MIBS-64 and evaluation under quantum model

MIBS is a lightweight block cipher with 32-round Feistel structure and 64 bit block length, including 64 bit and 80 bit master key versions. Based on the 12-round differential characteristics and the characteristics of key arrangement algorithm, this paper proposes 15-round differential analysis for MIBS-64 algorithm for the first time. This article also evaluates the differential analysis of the MIBS algorithm under the quantum model.
Research on the Collision Checking During the Boosters-Separation Based on Spatial Geometry Structure

Dynamic clearance calculation and collision checking during the elastic boosters-separation is essential to the design of separation scheme, which assesses the reasonability of the design. Traditional clearance calculation method has to traverse all the separation bodies and detect the minimal distance, which is extremely low in calculation efficiency. A new method is proposed in this paper to deal with the problem. First of all, the minimal distance between non-uniplanar lines and cylinders is derived based on the spatial geometry, and then based on this analytical result, the dynamic clearance calculation method combining the analytical and numerical approaches is proposed and validated. The lateral separation process of a launch vehicle with strapped-on boosters is studied, the separation dynamic clearance of which is calculated with both the traditional method and the proposed method. It is shown through the comparison that the new method can assure the required accuracy and drastically reduce the calculation time. The proposed method can be applied to the separation scheme design of launch vehicles with strapped-on boosters, and provide basis for the design and separation simulation.
End-to-end Indonesian Speech Synthesis Based On Transfer Learning And Alternate Training

The end-to-end speech synthesis system is superior to traditional statistical parameter synthesis methods in many aspects and obviously improves the naturalness of synthesized speech, but it still needs improvement. For example, it needs to use a larger-scale high-quality text-speech pairs training model, and the cumulative error formed in the synthesis process will affect the quality of long speech. In view of the characteristics of Indonesian and its current situation of low resources, this paper uses English corpus to pre-train the end-to-end speech synthesis model, then uses Indonesian corpus to transfer and learn the model parameters. On this basis, to overcome the influence of accumulated errors on synthetic long speech, this paper proposes an alternative training method and then studies the optimal alternative training scheme. The experimental results show that when using small-scale Indonesian text-speech pairs as training data sets, the mean Mel cepstral distortion of Indonesian long sentences speech synthesis decreases to 17.54, significantly lower than that of the baseline Tacotron2 system. The results of codec attention alignment coordinate figure and synthetic speech recognition also confirm the effectiveness of the proposed method.
20NAR1 - An Alternative NARS Implementation Design

An implementation of a Non-Axiomatic Reasoning System-inspired system is presented in this paper. This implementation features a goal system which features deep derivation depths, which allows the system to solve moderately complicated problems. The reasoner is utilizing Non-Axiomatic Logic for procedural and non-procedural reasoning. Most of the internal tasks are done under the Assumption of Insufficient Knowledge and Resources fulfilling various timing and resource constraints.
An sEMG Gesture Recognition Algorithm Based on Improved MobileNet

Surface electromyography (sEMG) signals are generated by the potential changes of muscle fibers. Gesture recognition based on sEMG is a research hotspot in action recognition. And it has been widely used in many fields such as rehabilitation medicine and human-computer interaction. Convolutional neural networks, a typical class of deep learning neural networks, are often applied to sEMG gesture recognition currently. However, these methods have problems of too many parameters, large computation, and long running time. To these problems, MobileNet, a lightweight network model with fewer parameters, is adopted in this paper, and an sEMG gesture recognition algorithm based on improved MobileNet is proposed. The improved MobileNet uses MobileNet as the main architecture, and introduces a genetic algorithm to optimize the parameters in the MobileNet. Simulation results show that, the proposed recognition algorithm reduces the parameter amount and the computation burden without loss of accuracy.
Popular deep learning image classifiers

The state-of-the-art image classification techniques have emerged from submissions by academics and industry leaders to the ImageNet large-scale visual recognition challenge (ILSVRC). ILSVRC is an annual contest aimed at developing and improving computer vision techniques on a subset of the ImageNet dataset. This chapter addresses eight well-known image classification deep learning architectures that have been ranked first in the ILSVRC in different years along with their Keras codes.
Contrastive Learning in Neural Tensor Networks using Asymmetric Examples

Neuro-Symbolic models combine the best of two worlds, knowledge representation capabilities of symbolic models and representation learning power of deep networks. In this paper, we develop a Neuro-Symbolic approach to infer unknown facts from relational data. A well-known approach is to use statistical relational models such as Markov Logic Networks (MLNs) to perform probabilistic inference. However, these approaches are known to be non-scalable and inaccurate for large, real-world problems. Therefore, given symbolic knowledge, we train a Neural Tensor Network (NTN) to learn representations for symmetries implied by the symbolic knowledge. Further, since the data is interconnected, predicting one fact can positively or negatively impact the prediction of other facts. Therefore, we train the NTN using open-world semantics over multiple possible worlds, learning to represent symmetries in each world. We evaluate our approach in several real-world benchmarks comparing with state-of-the-art relational learning methods, Neuro-Symbolic methods and purely symbolic methods clearly illustrating the generality, accuracy and scalability of our proposed approach.