Reviewerly/exHarmony huggingface dataset:
High-performance blockchain system for fast certification of manufacturing data

Abstract Integration of blockchain systems into industrial applications show promise in increasing security, trust, and transparency along the value-chain during product and process tracking. However, current solutions suffer performance deficiencies, or often disregard legacy devices still in operation. We propose a blockchain system built upon an IoT architecture that is secure, modular, easily scalable, and deployable for fast certification of manufacturing data, compatible with current industrial landscapes. First, the proposed architecture is presented along with elements required to manage network functions. Second, easing integration with existing manufacturing solutions, custom APIs are created and subsequently explained. This grants the platform plug-and-play capabilities, requiring minimal hardware and software configuration for deployment. Lastly, a prototype is designed to validate the solution, concluding the viability of the proposed architecture as a fast and secure certification method of manufacturing data.
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning From Demonstrations

We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations. Our approach uses the Karush-Kuhn-Tucker (KKT) optimality conditions to determine where on the demonstrations the constraint is tight, and a scaling of the constraint gradient at those states. We then train a GP representation of the constraint which is consistent with and which generalizes this information. We further show that the GP uncertainty can be used within a kinodynamic RRT to plan probabilistically-safe trajectories, and that we can exploit the GP structure within the planner to exactly achieve a specified safety probability. We demonstrate our method can learn complex, nonlinear constraints demonstrated on a 5D nonholonomic car, a 12D quadrotor, and a 3-link planar arm, all while requiring minimal prior information on the constraint. Our results suggest the learned GP constraint is accurate, outperforming previous constraint learning methods that require more <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge.
Towards Stopwords Identification in Tamil Text Clustering

Now-a-days, digital documents have become the primary source of information.Therefore, natural language processing is widely utilized in information retrieval, topic modeling, document classification, and document clustering.Preprocessing plays a significant role in all of these applications.One of the critical steps in preprocessing is removing stopwords.Many languages have defined their list of stopwords.However, a publicly available stopwords list isn't available for the Tamil language since it is under-resourced.This study identified 93 general and some domain-specific stopwords for sports, entertainment, local and foreign news by analyzing more than 1.7 million Tamil documents with more than 21 million words.Also, this study shows that removing stopwords improves the accuracy of a Tamil document clustering system.It showed an improvement of 2.4%, 0.95% in the F-score for TF-IDF with one pass algorithm and FastText with the one-pass algorithm, respectively.
Improving Access to Free Online Legal Information Through Universal Design: User Personas, User Journeys, a Proposal, and a Prototype

This article uses the design thinking methods of user personas and user journeys to consider how to improve the user experience of searching for free legal information online. A universal design for free legal information websites would accommodate people with varying levels of online search skills and familiarity with the law, thus enhancing access to justice. To realize a vision of robust and user-friendly access to free online legal information, this article presents a prototype for a network of official legal information websites with the same look, layout, and functionality—all designed to be useful, inclusive, and simple to use.
With or Without Blockchain? Towards a Decentralized, SSI-based eRoaming Architecture

Fragmentation and limited accessibility of charging infrastructure impede the adoption of electric vehicles.To improve the availability of charging infrastructure independent of providers, eRoaming offers a promising solution.Yet, current eRoaming systems are typically centralized, which raises concerns of market power concentration.While the use of blockchain technology can obviate such concerns, it comes with significant privacy challenges.To address these challenges, we explore a combination of blockchain with self-sovereign identity.Specifically, we apply a design science research approach, which helps us to identify requirements, derive a conceptual architecture, and deduce design principles for decentralized eRoaming and beyond.We find that blockchain may best leverage its benefits when it takes a backseat as a public registry for legal entities.Moreover, we find that the use of self-sovereign identities could improve compliance with privacy regulations, but they should not be overused.
Context-Aware Interaction Network for Question Matching

Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations.However, regular cross-attention focuses on word-level links between the two input sequences, neglecting the importance of contextual information.We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship.Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information when aligning two sequences, and (2) a gate fusion layer to flexibly interpolate aligned representations.We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results.Experiments on two question matching datasets and detailed analyses demonstrate the effectiveness of our model.
Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

Constitutional processes are a cornerstone of modern democracies. Whether revolutionary or institutionally organized, they establish the core values of social order and determine the institutional architecture that governs social life. Constitutional processes are themselves evolutionary practices of mutual learning in which actors, regardless of their initial political positions, continuously interact with each other, demonstrating differences and making alliances regarding different topics. In this article, we develop Tree Augmented Naive Bayes (TAN) classifiers to model the behavior of constituent agents. According to the nature of the constituent dynamics, weights are learned by the model from the data using an evolution strategy to obtain a good classification performance. For our analysis, we used the constituent agents’ communications on Twitter during the installation period of the Constitutional Convention (July–October 2021). In order to differentiate political positions (left, center, right), we applied the developed algorithm to obtain the scores of 882 ballots cast in the first stage of the convention (4 July to 29 September 2021). Then, we used k-means to identify three clusters containing right-wing, center, and left-wing positions. Experimental results obtained using the three constructed datasets showed that using alternative weight values in the TAN construction procedure, inferred by an evolution strategy, yielded improvements in the classification accuracy measured in the test sets compared to the results of the TAN constructed with conditional mutual information, as well as other Bayesian network classifier construction approaches. Additionally, our results may help us to better understand political behavior in constitutional processes and to improve the accuracy of TAN classifiers applied to social, real-world data.
Effect of Mobile Learning on the Optimization of Preschool Education Teaching Mode under the Epidemic

Mobile learning has become an efficient way to meet the needs of work learning in the epidemic situation because of its convenience, flexibility, and freedom. This paper studies and discusses the impact of mobile learning on learning education and preschool education in the epidemic. A mobile learning community resource sharing algorithm is used to explore the speed of the online learning community to obtain learning resources. The advantages of online learning are analyzed by comparing the speed of learning resources obtained in ordinary groups. In this research, the random offloading algorithm (ROA) is proposed to analyze the student response. The results revealed that majority of the students believed that mobile learning helps in learning subjects to a greater extent.
Collaborative Manipulation of Spherical-Shape Objects with a Deformable Sheet Held by a Mobile Robotic Team

In this paper, we present a method to model and manipulate a spherical object using a deformable sheet held by a team of mobile robots. The proposed modeling method captures the dynamic behavior of the spherical object on the deformable sheet. By strategically changing the position of the robots, the resulting sheet deformation is used to manipulate the motion of the spherical object. The control system uses the model to track the desired position and orientation simultaneously. Simulation results are presented to demonstrate the control design's performance and verify the successful manipulation of the object.
Towards Label-Agnostic Emotion Embeddings

Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few wellresourced but much more under-resourced) natural languages and text genres (e.g., product reviews, tweets, news).The resulting heterogeneity makes data and software developed under these conflicting constraints hard to compare and challenging to integrate.To resolve this unsatisfactory state of affairs we here propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures.Experiments on a wide range of datasets indicate that this approach yields the desired interoperability without penalizing prediction quality.
An Activity Theory-Based Approach for Context Analysis, Design and Evolution

This paper presents a new interdisciplinary approach to support context modeling in context-awareness software developments. The premise of this approach relies on the idea that understanding a complex socio-technical ecology, while adapting the software to its behavior and evolution, is a primary challenge to address. Thus, the paper proposes an activity theory-based approach to aid in the conception, design, development, and evolution of emerging context-aware socio-technical ecologies. The concepts and notations used by the proposed approach are illustrated through a proof of concept that demonstrates the essential ideas and their use in real scenarios. Also, the feasibility of this approach is measured empirically through an experiment. Preliminary results show how, for a context-aware software design and development team, the proposal provides a better understanding of context than alternatives and helps to outline context models by establishing relationships and interactions between socio-technical components and by anticipating potential conflicts among them. The key ideas of the proposed approach result in the ability to analyze and model social and technological contexts around perpetually evolving system ecologies as useful representations for understanding operating environments closely tied to human actions, with software as a mediator component.
Burst Imaging for Light-Constrained Structure-From-Motion

Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this letter we develop an image processing technique for aiding 3D reconstruction from images acquired in low light conditions. Our technique, based on burst photography, uses direct methods for image registration within bursts of short exposure time images to improve the robustness and accuracy of feature-based structure-from-motion (SfM). We demonstrate improved SfM performance in challenging light-constrained scenes, including quantitative evaluations that show improved feature performance and camera pose estimates. Additionally, we show that our method converges more frequently to correct reconstructions than the state-of-the-art. Our method is a significant step towards allowing robots to operate in low light conditions, with potential applications to robots operating in environments such as underground mines and night time operation.
Asymptotically Tight Lower Bounds in Anonymous Broadcast Encryption and Authentication

Broadcast Encryption (BE) is a cryptosystem that allows a sender to specify recipients so that only the specified recipients can perform decryption. Anonymity, which is one of additional but important security requirements of BE, guarantees that no information of the designated recipients is leaked from ciphertexts, and several BE schemes with anonymity (ANO-BE) have been proposed so far.Kiayias and Samari (IH 2013) analyzed a lower bound on the ciphertext size required for ANO-BE. In their analysis, they derived the lower bound under the assumption that ANO-BE schemes meets a special property. However, it is unclear whether the special property holds for existing ANO-BE schemes. In other words, their analysis is insufficient to show that the existing ANO-BE schemes achieve the optimal ciphertext size.In this paper, we derive a lower bound on the ciphertext size in ANO-BE, assuming only properties that most existing ANO-BE schemes satisfy. In our analysis, we newly define several properties abstracted from existing (even non-anonymous) BE schemes and carefully analyze them to replace the Kiayias–Samari assumption with ours. As a result, we show that the existing ANO-BE schemes achieve the optimal ciphertext size. We further show that our analysis can be extended to the authentication setting. Specifically, we first derive a lower bound on the authenticator size required for anonymous broadcast authentication.
Fast Cooperative Trajectory Generation of Unmanned Aerial Vehicles Using a Bezier Curve-Based Shaping Method

This study uses a Bezier curve-based shaping (BCBS) approach based on the dynamic model to quickly generate 3D cooperative trajectories for unmanned aerial vehicles (UAVs). It can facilitate the solution of the flight trajectory problem that occurs after the reallocation of mission points. Trajectory coordinates are expanded based on the Bezier curves that naturally satisfy boundary conditions (BCs); trajectories with the minimum flight time and those satisfying the dynamic constraints and collision-free conditions can be obtained by adjusting the Bezier coefficients. A three-UAV flight simulation is carried out to verify the effectiveness of the proposed method. Further, the performance of the proposed method is compared with that of the Gauss pseudospectral method (GPM). The simulation results of the BCBS method are used as the initial values for GPM optimization. It is demonstrated that the BCBS method requires a lower computation time than the direct solver, which is only 0.07% of the latter, and obtains similar optimization results (3.34% difference). This is considerably important for the rapid generation of optimized trajectories with the limited computing power of onboard computers. Furthermore, this method is expected to achieve online collaborative trajectory generation for multiple UAVs in view of its high computational efficiency.
Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier

In every product sold on the E-commerce platform, there is a review column from consumers who have made transactions on the products. These reviews are in the form of comments and ratings (stars from one to five) written and given by consumers based on their assessment of the products purchased. With the product evaluation feature based on the rating, the consumer can find out how good or bad the quality of the product is. However, a problem arises when some consumers give negative comments with five stars or vice versa. This causes the product assessment feature based on the rating to be less good so that it does not represent the real value. Therefore, to determine the quality of the product, sentiment analysis was carried out using the TF-IDF method and the Naive Bayes Classifier based on reviews from buyers. The data collected is 1000 reviews which are divided into 700 training data and 300 test data. The next stage is the preprocessing text such as case folding (converting uppercase letters to lowercase), tokenizing (separating sentences into single words), stopwords (removing tokenizing conjunctions that have nothing to do with sentiment analysis), stemming (changing words into basic word forms), and word weighting with TF-IDF. The last step is to classify. Based on the classification results obtained an accuracy rate of 80.2223%.
Adaptive Multiple Layer Retinex-Enabled Color Face Enhancement for Deep Learning-Based Recognition

Face image captured under uncontrolled illumination conditions is one of the most significant challenges for real-world human face recognition systems. To overcome this problem, we proposed a novel method called adaptive multiple-layer retinex-based color face enhancement (AMRF) to enhance the face images. Firstly, we use an associative filter to decompose a color face image into illumination and reflectance components at multiple layers. Then, the illumination components in each layer are adjusted automatically by multiplying with corresponded illumination compensation coefficients calculated through a referenced Gaussian template. The enhanced color face image is finally obtained by composing the compensated illumination components with the integrated reflectance component based on the Retinex theory. The experiment was performed on four popular color face datasets: LFW, IJB-C, CMU Multi-PIE, and CMU-PIE. Our proposed method makes face images more precise, natural, and smooth. The experiment results show that AMRF’s image quality assessment scores are significantly better than the original and other enhanced methods’ images. Furthermore, AMRF considerably improves the recognition accuracy of deep learning-based face recognition models, such as FaceNet, and ArcFace. Finally, our proposed method also saves computational time comparing the other techniques.
Towards a Taxonomy of Information Security Policy Non-Compliance Behavior

Due to the increasing digitalization of our society, IT security professionals must implement even more effective security measures to meet the growing information security requirements of their organizations.To target and effectively deploy these measures in the best possible way, they must consider different types of behaviors that might lead to information security threats.Regarding this issue, current research offers little for clarity to security professionals when it comes to understanding and differentiating the various types of behavior.Therefore, this research aims to develop a taxonomy to classify different types of information security policy non-compliance behavior.Our results present a taxonomy with five dimensions, each containing mutually exclusive and collectively exhaustive characteristics.Our results provide a basis for a more specific analysis of different types of information security policy non-compliance behavior and can be used for more comprehensive development and analysis of appropriate security measures.
A Segmentation Scheme for Knowledge Discovery in Human Activity Spotting

Spotting the target activity in a sequence of activities and transitions without applying a predetermined window size is a challenging task. The sliding window method, which is the typical approach in segmenting the continuous data stream, operates with optimal segment sizes chosen considering the type and duration of the activities. Nevertheless, activity type and duration are usually unknown to the detection unit in practice. In this study, we proposed the nonpredetermined size windowing (NSW) scheme to spot the target activity performed in a sequence of unseen activities. NSW is built on classifying progress-based features in multilayer training and prediction stages where the time-domain progress is expressed in terms of polynomials. Thus, it operates without incorporating the information regarding duration and type of the activities. We verified our method with a proof-of-concept use case where data are acquired by a single wrist-worn 3-D accelerometer. We compared our method against fixed size windowing performed with varying window sizes and feature extraction schemes; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">windowed energy</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">peak frequency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shannon entropy</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wavelet entropy</i> . Our method outperforms the compared schemes, reaching a median accuracy of 89% and a median true positive rate of 1 in both intrasubject and intersubject cases based on an independent test set where each test instance contains a sequence of nine different activities.
UCBIS: An improved consortium blockchain information system based on UBCCSP

Blockchain technologies have been applied in many areas, from economics, the internet of things to the industrial internet. In order to solve the issue that the Hyperledger Fabric does not currently support Chinese Commercial Cryptographic (CCC) algorithms, we extended the Blockchain Cryptographic Service Provider (BCCSP) module in the Hyperledger Fabric by upgrading the original BCCSP module to support the CCC algorithms SM2 and SM3. Furthermore, we designed a transaction process by using UBCCSP (Upgraded BCCSP), and a new smart contract also has been presented. After that, an improved consortium blockchain information system based on UBCCSP named UCBIS (Consortium Blockchain Information System based on UBCCSP) is proposed. In the Hyperledger Fabric transaction process, the identity information and transaction data are protected by the SM2 and SM3 algorithms, moreover, SM3 is also used in the construction process of smart contracts. Our smart contracts reduce the total data amount and improve query efficiency. Finally, the information query system based on UBCCSP is implemented. After being tested and analyzed, the average time for every query is only 31.162 ms in the blockchain system, which has better performance and higher query efficiency.