Reviewerly/exHarmony huggingface dataset:
Self-Organizing Interval Type-2 Fuzzy Neural Network With Adaptive Discriminative Strategy

Covariate shift is a critical issue of interval type-2 fuzzy neural networks (IT2FNNs) due to the distribution discrepancy between training and testing samples. In this situation, IT2FNNs usually struggle to identify potential features from samples with explicit inductive biases. To address this problem, a self-organizing IT2FNN with an adaptive discriminative strategy (ADS-SOIT2FNN) is developed to maintain the identification performance in the presence of covariate shift. First, a granularity-based metric (GM), using higher order statistics of local samples, is designed to distinguish the distribution discrepancy caused by covariate shift. The multiple kernels incorporated into GM are able to cover the sample features of the whole Hilbert space. Second, a self-organizing strategy, associated with GM-based discriminative information, is presented to alleviate the structural bias by growing and pruning fuzzy rules. Then, a compact structure of ADS-SOIT2FNN is achieved to adapt to the covariate shift of samples and further strengthen its inductive ability. Third, an adaptive risk mitigation learning algorithm (RMLA) is introduced to update the parameters of ADS-SOIT2FNN. RMLA can regulate the derivatives of parameters with arbitrary distribution samples, which is beneficial for maintaining the global accuracy by relieving the risk of parameter biases. Finally, the effectiveness of ADS-SOIT2FNN is verified by some experiments for identifying nonlinear systems with covariate shift.
Integrating Sensor Fusion with Pose Estimation for Simulating Human Interactions in Virtual Reality

Virtual Reality (VR) enables users to interact with a simulated environment through a head-mounted device (HMD) and two hand controllers. These help in tracking the user’s head and hand locations and movements but have no references to the rest of the body. Though these movements can be tracked using additional equipment like trackers and bodysuits, being tightly coupled with hardware would make this an inconvenient as well as an expensive option. Also, other existing computer vision solutions require calibration and expensive depth-based cameras. In this paper, we present a novel approach of using sensor fusion techniques integrated with computer vision algorithms as an alternate solution to position human joints in the virtual environment and simulate their movements. The human landmark identification and pose estimation are achieved using ML algorithms for computer vision. These landmarks are mapped to an avatar in virtual space using geometric transformations and inverse kinematics. The use of sensor fusion ensures the correctness of scale and transform operations. The HMD position helps pivot the estimated pose and set the view for the user. The hand controller feed is used to control and verify the position of the hand with respect to the upper body. Through sensor fusion, we can bypass the complicated setup required by other Computer Vision (CV) approaches. The solution is built using open source tools and frameworks and the rest of the paper discussed the related work, design overview, and results in detail.
Generator Matrices of Quasi-cyclic Codes over Extension Fields Obtained from Gröbner Basis

Quasi-cyclic codes over finite fields are an important class of linear block codes. A fundamental problem in the theory of these codes is to describe their algebraic structure. In this paper it is shown that every quasi-cyclic code is the subfield code and the trace code of a quasi-cyclic code over an extension field. The latter is defined by a parity check matrix obtained from a spectral analysis of a reduced Gröbner basis of the former. Moreover, it is shown that the quasi-cyclic code over the extension field and the one under consideration have the same length, dimension and minimum Hamming distance. Furthermore, we show that under certain conditions it is possible to construct a generator matrix of the quasi-cyclic code over the extension field using similar techniques to construct its parity check matrix. We illustrate that this construction is attainable for some good quasi-cyclic low density parity check codes like the [155, 64, 20] binary Tanner code.
RF Fingerprint Identification of Commercial UAV in Outdoor Environment

In recent years, commercial UAVs (unmanned aerial vehicles) have been widely used in various scenarios, and at the same time, they are also facing serious security threats. The traditional cryptography-based authentication mechanism can hardly meet the needs of new scenarios and applications, so RF fingerprint recognition technology with low complexity and high security has gradually become a research hotspot. There is a large literature on this aspect of research and experimentation. However, most of the data used in experiments are from laboratories or open outdoor environments with LoS (Line of Sight) signals. In practical application scenarios, commercial UAVs may work in some outdoor environments with complex interference factors. Therefore, this work proposes the verification of the RF fingerprint recognition capability of commercial UAVs in complex outdoor scenarios.
Study of Taiwanese White Dolphins Detection and Tracking Techniques by utilizing Autonomous Unmanned Aerial Vehicles

The paper describes the autonomous virtual UAV (Unmanned Aerial Vehicle) using a visual detection model to track the Taiwanese white dolphins. In this research, the SITL simulator (Software in the Loop) runs ArduPilot on the machine. ArduPilot is an autopilot system for autonomous vehicles, which is installed on the virtual drone. The simulation helps evaluate the detection model and the tracking system prior we launch the drone out over the ocean where the consequences of mistakes can be very expensive. The customized YOLO v4 model has been trained on the Taiwanese White Dolphin Image dataset 201S owned by Cetacean Lab in NTU. The model has the ability to detect the side view of the white dolphin under 702 pixels @0.5AP (Average Precision) in the real-sea footage. The model’s proper detection condition such as the distance between the white dolphin and the drone, the height above the horizon, and the pitching angle of the camera, is estimated by the simulation in the paper as well. A white dolphin 3D model is sculpted and textured based on the real white dolphin called Doufu with Blender software. In the Gazebo Simulator, the 3D dolphin model moves in the shapes of a rectangle, circle, and figure-eight with a constant velocity of 10 knots on the water. With the frame caught on the virtual drone camera and the detection model, the virtual drone can track the dolphin without losing it under certain conditions. The Taiwanese White dolphin (subspecies of Sousa chinensis) is listed as critically endangered [1]. And the population is decreasing year by year. The real-time monitoring of the Taiwanese White dolphin is still unlikely to be realized unless we launch the drones, send the hydrophones to the sea, and establish the individual PHOTO ID and AUDIO ID. It is hoped that people can take advantage of this system, run different varieties of simulations, and Figure out a better algorithm running through those possibilities to help monitor and conserve the white dolphins.
Low Rank and Semi-Nonnegative Tensor Factorization for Hyperspectral Imagery Denoising

Hyperspectral image denoising can play an important role in addressing the issue of preprocessing massive high-dimensional hyperspectral data for subsequent object detection or classification. This paper presents a novel low-rank and semi-nonnegative tensor factorization method for HSI denoising. The framelet regularization is introduced to constrain the reduced-dimensionality factor, rather than directly regularizing HSI itself in the low-rank and non-negative tensor factorization model. Thus, our method preserves the details and geometric features of restored HSI in the spatial domain and demand much less calculation and computer memory. Extensive experimental results show that our method is superior to other existing methods for HSI denoising in simulated benchmark datasets. Our source code is available at: https://github.com/misteru/LRSNTF.
Experiments on Code Clone Detection and Machine Learning

The latest machine learning techniques are already capable of solving extremely complex problems and outperforming conventionally implemented software. Also in code clone detection, there is an increasing number of approaches that work with machine learning. In this paper, a machine learning model is trained with dominator trees generated from the bytecode of the BigCloneBench benchmark and similar performance results to previous work can be produced. However, further experiments using a rigorous approach with strict separation of data sets show that these previous results have little practical relevance.
Impact of Design Decisions on Performance of Embarrassingly Parallel .NET Database Application

The implementation of parallel applications is always a challenge. It embraces many distinctive design decisions that are to be taken. The paper presents issues of parallel processing with use of .NET applications and popular database management systems. In the paper, three design dilemmas are addressed: how efficient is the auto-parallelism implemented in the .NET TPL library, how do popular DBMSes differ in serving parallel requests, and what is the optimal size of data chunks in the data parallelism scheme. All of them are analyzed in the context of the typical and practical business case originated from IT solutions which are dedicated for the energy market participants. The paper presents the results of experiments conducted in a controlled, on-premises environment. The experiments allowed to compare the performance of the TPL auto-parallelism with a wide range of manually-set numbers of worker threads. They also helped to evaluate 4 DBMSes: Oracle, MySQL, PostgreSQL, and MSSQL in the scenario of serving parallel queries. Finally, they showed the impact of data chunk sizes on the overall performance.
Interactive Double Graph Convolutional Networks for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis aims to judge the sentiment polarity of specific aspects in comments. Recent meth-ods use graph neural networks based on dependency trees to obtain the relationship between aspects and opinion words by using syntactic information. However, these models ignore the situation that the results of dependency tree parsing are incorrect and the sentences without significant syntactic structure. To solve these problems, in this paper, we propose an interactive double graph convolution networks (Inter-DGCN) model. We reconstruct the dependency tree according to the syntactic in-formation and construct the syntactic graph convolution module. Moreover, we construct the semantic graph convolution module, which uses multi-head self-attention to represent the semantic correlation between words. In addition, we add interactive feature fusion to learn the dual graph convolutional network interactively. Experimental results on three datasets demonstrate the effectiveness and advancement of our proposed model.
Dynamic Negative Example Construction for Grammatical Error Correction Using Contrastive Learning

Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms. Due to the difference of error type distribution and error density, current grammatical error correction systems may over-correct writings and produce a low precision. To address this issue, in this paper, we propose a dynamic negative example construction method for grammatical error correction using contrastive learning. The proposed method can construct sufficient negative examples with diverse grammatical errors, and can be dynamically used during model training. The constructed negative examples are beneficial for the GEC model to correct sentences precisely and suppress the model from over-correction. Experimental results show that our proposed method enhances model precision, proving the effectiveness of our method.
Feasibility Test of Using Disposable Baby Diaper Waste as Raw Material for Fiberboard

The high number of births in Indonesia causes the waste of disposable baby diapers to be higher. Some people have used disposable baby diaper waste as a more valuable item, but this is still not enough to overcome the problems caused by disposable baby diaper waste. This study aims to increase the value of disposable baby diaper waste by utilizing it as a raw material for fiberboard. Experiments were carried out to produce fiberboard made from disposable baby diaper waste combined with citric acid as an adhesive in various compositions and pressing times. The fiberboard is tested on several parameters based on the National Standardization Agency of Indonesia standard requirements. From the results of the fiberboard testing, all treatments met the National Standardization Agency of Indonesia's legal requirements. The best treatment chosen was fiberboard with a compressed time of 15 min, and the composition of raw materials and adhesives was 10:2.
PROSES KREATIF MENULIS USAHA INTERPRETASI NILAI PENDIDIKAN DALAM KARYA SASTRA

The idea in this paper is an effort to optimize learning to appreciate literary works during the Pandemic. This paper will map the problems of literary learning during the Covid-19 pandemic by looking at the creative process of writing as an effort to interpret the value of education in literary works. The problem of learning literary appreciation carried out by teachers and students during this Pandemic will be revealed some of the creative processes of writing and its steps. By writing literary works, the hope for increasing the understanding and interpretation of literary works can be maximized and the purpose of appreciation in shaping educational values can be achieved.
How Students Design Visual Interfaces for Information Visualization Tools

Creating information visualization tools is a challenging task for which expertise in many research fields is required to get a suitable result for the tasks at hand, for example, to interactively explore data sets for data patterns. To reach a good outcome, it is important to learn how to combine the different concepts from all the related disciplines and to understand what the no-goes are to avoid possible design flaws that hinder a data explorer at solving the tasks at hand. In particular, the newcomers in information visualization have to be taught to follow the rules in visualization, human-computer interaction, but even more the well-known and established design criteria to avoid the same mistakes as others did before. In this paper we describe a teaching concept to guide bachelor students with nearly no experience in information visualization to design their own visual interfaces focusing on data visualization problems. We illustrate the results from the course in several application examples showcasing the visual designs that students came up with.
A Traceability System for Processed Products Based On Blockchain Technology

The standard tracking system has problems where it is controlled by a single authority, non-transparent information, corrupted data. To address these issues, the article contributes to the development of a tracking technique using block-chain for storing along with investigating data in a processed supply food chain supply. The tracking, verified data, dissemination, and openness and dependability of tracking details are all features of block-chain technology. Safety, quality, uniqueness, and the guarantee of critical processes in convenience and supply chains are all highlighted in the global manufacturing and delivery of processed products.In the paper, a blockchain inspired way to monitoring processed products and tracking across the food supply chain that dramatically improves commercial transactions. In products supply chain ecosystem it is not secure and cannot be trusted, where the supply chain ecosystem can be accessed by any hacker hence there is no security for these files. There should be a solution adapted to increase the security of supply chain ecosystem transaction. Our proposed solution eliminates the requirement for a dependable chief executive, negotiates and distributes operational records, increasing efficiency and security. The suggested method details show how to use a blockchain to track and manage every activity among every participants of ecosystem.
Improved Genetic-Ant Colony Fusion Algorithm

An improved genetic-ant colony fusion algorithm is proposed to improve the search speed of path planning and to obtain the optimal solution. Combining the advantages of the two algorithms, adaptive fetching is used for selection probability, crossover probability and variation probability respectively for the genetic algorithm to globally search for the optimal path. To address the problem that the number of iterations in the genetic algorithm relies too much on subjective experience, we propose to derive the evolutionary degree from the fitness function as a strategy to control the conversion of the algorithm. Finally, the optimal solution searched by the genetic algorithm is used as the value of the initial pheromone of the ant colony algorithm, and the crossover operation of the genetic algorithm is added to the ant colony algorithm, which can optimize the search speed . The experiments show that the fusion strategy can improve the path planning search speed and the accuracy of the candidate solutions.
Research on Texture Trait Extraction Algorithm of Folk Art Patterns Based on Computer Image Recognition

As a basic property of object surface, texture is broadly used in image analysis because it can well characterize the image. Texture is the most vital way of visual perception and a main way for humans to distinguish various objects. In the past few days, it has become a study hotspot in the domain of image conduct. As a carrier of message, image has the characteristics of intuition, large amount of message, and convenient communication between various countries. It is an vital part of network multimedia. Text-based image retrieval is the basis of content-based image retrieval. It is difficult to imagine the workload of manually interpreting image message, and its feasibility is also questionable. The means used in this article is computer image recognition, which uses visual sensors to replace human vision, receives external visual signals through visual sensors, converts the signals into visual images, and then processes the images to obtain message of interest that is similar to human visual perception. . The study in this article shows that the algorithm in this article can achieve a higher recognition ratio when the trait dimension is low. The recognition ratio of the means in this article is higher than that of the traditional algorithm.
Learning Rate and Regularization Coefficient-Free Latent Factor Analysis via PSO

A big-data-related application commonly has numerous nodes, e.g., users and items in a recommender system [1–5]. With the exponential growth of involved nodes, it is impossible to obtain their whole interaction relationships, e.g., it is impossible for a recommender system’s individual user to touch all of its items [6, 7]. For instance, the Epinions Matrix [8] is a typical HiDS matrix. It contains 631,064 ratings by 51,670 users on 83,509 different entries, with a data density of 0.015% only. Hence, the resultant interactions can be described by a high-dimensional and sparse (HiDS) matrix [9–18].
Selection of the Composition of the Included Generating Equipment Considering the Reliability Factor

At present, ensuring reliability is an integral part of maintaining the high efficiency of operation of electric power equipment. The reliability factor can be considered and considered at different stages of solving the problem of optimizing the operation of equipment for thermal power plants, this article will consider the stage of applying for choosing the composition of the main generating equipment, considering reliability. The method for choosing the composition of the main generating equipment, considering the reliability criterion, is described. A technique for calculating the reliability characteristics of equipment and taking them into account when a power plant enters the market is presented. The optimal distribution of the load and the choice of the composition of the equipment, considering the criterion of reliability, are analyzed using the example of different loads of power units.
Towards Data Redaction in Bitcoin

A major issue for many applications of blockchain technology is the tension between immutability and compliance to regulations. For instance, the GDPR in the EU requires to guarantee, under some circumstances, the right to be forgotten. This could imply that at some point one might be forced to delete some data from a locally stored blockchain, therefore irreparably hurting the security and transparency of such decentralized platforms. Motivated by such data protection and consistency issues, in this work we design and implement a mechanism for securely deleting data from Bitcoin blockchain. We use zero-knowledge proofs to allow any node to delete some data from Bitcoin transactions, still preserving the public verifiability of the correctness of the spent and spendable coins. Moreover, we specifically use STARK proofs to exploit the transparency that they provide. Our solution, unlike previous approaches, avoids the complications of asking nodes to reach consensus on the content to delete. In particular, our design allows every node to delete some specific data without coordinating this decision with others. In our implementation, data removal can be performed (resp., verified) in minutes (resp., seconds) on a standard laptop rather than in days as required in previous designs based on consensus.
Human-Centered Learning Engineering for the Emerging Intelligence Augmentation Economy

We explore the intersection of human-centered engineering design methodologies, applied learning sciences, and data (instrumentation & analytics) realized in the practice of learning engineering in response to an emerging intelligence augmentation economy. As the nature of human endeavor and productivity changes with the advancement of HCI and AI technologies we predict an “intelligence augmentation economy” will replace the knowledge economy. Just like the industrial economy and service economies before it, the IA economy will fundamentally change the nature of work and what people need to learn to be its contributors and beneficiaries. The demands of secondary and postsecondary education have already begun to shift from pre-career and preparation for life to mid-career adaptation, enrichment, and retraining [1]. The intelligence augmentation economy will require development of new skills for new kinds of collaborative work with intelligent agents. The lines between learning and working will blur, just as will the lines between the work of humans and the work of machines. Education institutions will be one source for life-long-learning in a world where work-embedded continuous adaptive learning is the norm. The learning engineering process provides a methodology for educational institutions and work-embedded training partners to engineer new kinds of learning experiences suited to the needs of the intelligence augmentation economy. These new learning experiences will take full advantage of technologies such as augmented reality and intelligent agents. We draw ideas from several chapters of the Learning Engineering Toolkit: Evidence-Based Practices from the Learning Sciences, Instructional Design, and Beyond [2]. We suggest a path to scaling HCI innovation through application of the learning engineering process to support a radically new future of learning and working in a new intelligence augmentation economy characterized by productivity gains through collaboration between people and intelligent agents.