Reviewerly/exHarmony huggingface dataset:
Evaluation of COVID-19 Information Provided by Digital Voice Assistants

<strong>Background:</strong> Digital voice assistants are widely used for health information seeking activities during the COVID-19 pandemic. Due to the rapidly changing nature of COVID-19 information, there is a need to evaluate COVID-related information provided by voice assistants, to ensure consumers' needs are met and prevent misinformation. The objective of this study is to evaluate COVID-related information provided by the voice assistants in terms of relevance, accuracy, comprehensiveness, user-friendliness and reliability. <strong>Materials and Methods:</strong> The voice assistants evaluated were Amazon Alexa, Google Home, Google Assistant, Samsung Bixby, Apple Siri and Microsoft Cortana. Two evaluators posed COVID-19 questions to the voice assistants and evaluated responses based on relevance, accuracy, comprehensiveness, user-friendliness and reliability. Questions were obtained from the World Health Organization, governmental websites, forums and search trends. Data was analyzed using Pearson's correlation, independent samples t-tests and Wilcoxon rank-sum tests. <strong>Results:</strong> Google Assistant and Siri performed the best across all evaluation parameters with mean scores of 84.0% and 80.6% respectively. Bixby performed the worst among the smartphone-based voice assistants (65.8%). On the other hand, Google Home performed the best among the non-smartphone voice assistants (60.7%), followed by Alexa (43.1%) and Cortana (13.3%). Smartphone-based voice assistants had higher mean scores than voice assistants on other platforms (76.8% versus 39.1%, p = 0.064). Google Assistant consistently scored better than Google Home for all the evaluation parameters. A decreasing score trend from Google Assistant, Siri, Bixby, Google Home, Alexa and Cortana was observed for majority of the evaluation criteria, except for accuracy, comprehensiveness and credibility. <strong>Conclusion:</strong> Google Assistant and Apple Siri were able to provide users with relevant, accurate, comprehensive, user-friendly, and reliable information regarding COVID-19. With the rapidly evolving information on this pandemic, users need to be discerning when obtaining COVID-19 information from voice assistants.
Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis

A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, until tuning sentiment value with tagged sentiment corpus. It begins by taking seed words from WordNet Bahasa that mapped with sentiment value from English SentiWordNet . The seed words are enriched by combining the dictionary-based method with words’ synonyms and antonyms, and corpus-based methods with word embedding for word similarity that trained in positive and negative sentiment corpus from online marketplaces review and Twitter data. The valence score of each lexicon is recalculated based on its relative occurrence in the corpus. We also add some famous slang words and emoticons to enrich the lexicon. Our experiment shows that the proposed method can provide an increase of 3.5 times lexicon number as well as improve the accuracy of 80.9% for online review and 95.7% for Twitter data, and they are better than other published and available Indonesian sentiment lexicons.
The effects of continuous integration on software development: a systematic literature review

Continuous integration (CI) is a software engineering technique that proclaims frequent activities to assure the software product health. Researchers and practitioners mention several benefits related to CI. However, no systematic study surveys state of the art regarding such benefits or cons. This study aims to identify and interpret empirical evidence regarding how CI impacts software development. Through a Systematic Literature Review, we search for studies in six digital libraries. Starting from 479 studies, we select 101 empirical studies that evaluate CI in the context of software development. We thoroughly read and extract information regarding (i) the CI environment, (ii) findings related to the effects of CI, and (iii) the employed research methods. We apply a thematic synthesis to group and summarize the findings. Existing research has explored the positive effects of CI, such as better cooperation, or negative effects, such as adding technical and process challenges. From our thematic synthesis, we identify six themes: development activities, software process, quality assurance, integration patterns, issues & defects, and build patterns. Empirical research in CI has been increasing over recent years. We found that much of the existing research reveals that CI brings positive effects to software development. However, CI may also bring technical challenges to software development teams. Despite the overall positive outlook regarding CI, we still find room for improvements in the existing empirical research that evaluates the effects of CI.
A Solution to Data Accessibility Across Heterogeneous Blockchains

Cross-heterogeneous blockchain interactions have been attracting much attention due to their application in depository blockchains mutual access and cross-blockchain identity authentication. Trusted access across heterogeneous chains is gradually becoming a hot challenge. In order to ensure cross-blockchain trusted access, the majority of the current works focus on on-chain notaries and the relay chain model. However, these methods have the following drawbacks: 1) notaries on the chain are more vulnerable to attacks due to their high degree of centralization, which causes off-chain users to lose their trust and thus exacerbates the off-chain trust crisis; 2) although the relay model involves multiple parties in maintenance and supervision and enjoys a more robust trust, the paticipatant nodes are relatively fixed, which impose a terrible dilemma that invalid nodes cannot participate in consensus formation in a timely manner, thus progressively disrupting the connectivity of the relay across heterogeneous chains and eventually reducing the rate of trusted mutual access. In this article, we propose a novel general framework for cross-heterogeneous blockchain communication based on a periodical committee rotation mechanism to support information exchange of diverse transactions across multiple heterogeneous blockchain systems. Connecting heterogeneous blockchains through committees has a more robust trust than the notary method. In order to eliminate the impact of downtime nodes in a timely manner, we periodically reorganize the committee and give priority to replacing downed nodes to ensure the reliability of the system. In addition, a message-oriented verification mechanism is designed to improve the rate of trusted intervisit across heterogeneous chains. We have built a prototype of the scheme and conducted simulation experiments on the current mainstream blockchain for message exchange across heterogeneous chains. The results show that our solution has a good performance both in inter-chain access rate and system stability.
Political Arabic Articles Classification Based on Machine Learning and Hybrid Vector

Recently, there was substantial growth in the opinion data and the number of weblogs in the world wide web (WWW). The capability for automatically determining an article’s political orientation might be of high importance in various fields ranging from academia to security. Yet, sentiment classification related to the weblog posts (especially the political ones), has been more complex in comparison to sentiment classification related to the conventional text. In the presented study, supervised machine learning along with feature extraction methods Term Frequency (TF) and five grams (unigram, bigram, trigram, 4-gram, and 5-gram) were combined to generate a hybrid vector that applied for the process of classification. Besides, for investigation purposes, Support Vector Machine (SVM), Naive Bayes (NB), KNearest Neighbor (KNN), and Decision Tree (DT) for the supervised machine learning were used. After conducting the tests, the results indicated that the NB with unigram provided results with accuracy (93.548%). Thus, the NB is extremely acceptable in the presented model.
Group-wise Inhibition based Feature Regularization for Robust Classification

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://github.com/LinusWu/TENET_Training.
Online Residual Quantization Via Streaming Data Correlation Preserving

Recently, the online retrieval task has been receiving widespread attention, which is closely related to many real-world applications. However, existing online retrieval methods based on hashing suffer from two main problems: a) the models tend to be biased towards the current streaming data due to unavailable history streaming data; b) when new streaming data comes in and the hashing functions have been updated, all history binary codes should be recomputed, which takes much computation burden. To address the above two issues, we propose a novel Online Residual Quantization (ORQ) method that can achieve efficient streaming data quantization via the small-scale residual quantization codebooks. For the first problem, we design a residual quantization module by learning multiple residual codebooks to quantize the float streaming data, which effectively reduces the quantization error and enables the binary codes to be easily reconstructed back to original float data. Then, with the reconstructed history data, a balanced affinity matrix is developed to model the semantic relationship, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> similarity and difference, between the history and current data distributions, which can prevent the model from being biased towards the current data distribution. For the second problem, when inputting current streaming data, only the residual codebooks should be updated, instead of the whole history binary codes in hashing-based methods, which significantly reduces the computation burden. Comprehensive experiments on six benchmarks demonstrate that ORQ yields significant improvements ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> 1.2% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4.9% in average mAP) compared to the state-of-the-art methods.
Lightweight Blockchain Framework using Enhanced Master-Slave Blockchain Paradigm: Fair Rewarding Mechanism using Reward Accuracy Model

Blockchains have become the prominent technology for logging records within a secured platform. However, the biggest challenge in existing blockchain technologies is unnecessary wastage of resources, such as electricity, due to network traffic and inefficient reward mechanisms during the blockchain mining process. To overcome this, we introduce a lightweight blockchain framework to reduce the cost of computationally intensive mining processes and network traffic while fairly rewarding concurrent miners. The proposed framework divides the traditional blockchain into two layers: master node(s) and slave agents (SAs). We develop mechanisms to (i) compute the reward accuracy of successful miners considering the significant factors of time taken to identify a number used only once (nonce) and their reward success rate history, (ii) ensure the reliability of new transactions with an equivalent proof of work consensus, (iii) handle SAs who identify a nonce value at the same time as other SAs while rewarding successful miners fairly, (iv) analyze the fairness of the reward accuracy model by catering to a successful miner who failed to receive a success reward to compensate for costs spent on the mining process and network traffic, and (v) reduce electricity usage and scalability problems during the integration of the blockchain and the Internet of Things (IoT) by subdividing the blockchain into separate shards. We find that a lower weight percentage of the time taken to identify a nonce has a more significant effect on reward accuracy than does a higher weight percentage. Further, our results show that the time taken to identify a nonce has a higher dependence on reward accuracy than on the effect of the reward success rate history. We compare our algorithm with the existing algorithms and found the same algorithm complexity O(N) in the Bitcoin and Ethereum blockchains. We determine the break-even point to compensate for the cost of the mining process and network traffic. Consequently, we enable the possibility of compensation for a successful blockchain miner who failed to be granted a reward. This motivates miners to verify and validate new transactions before a new transaction is added to the blockchain. We also adopt an adversary model to obtain the fraction of participating master nodes and SAs that must be compromised by the adversaries to compromise our Master–Slave blockchain (MSB). In this study, we use an electronic healthcare system to illustrate how the proposed MSB works. However, our MSB could be applied in many fields, including the IoT, supply chain management, energy, and commodity transactions.
MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models

Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials, a property known as sample efficiency. This research thus investigates the use of learned world models to improve sample efficiency. We present a novel multi-agent model-based RL algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO), utilizing the Centralized Learning for Decentralized Execution (CLDE) framework. CLDE algorithms allow a group of agents to act in a fully decentralized manner after training. This is a desirable property for many systems comprising of multiple robots. MAMBPO uses a learned world model to improve sample efficiency compared to model-free Multi-Agent Soft Actor-Critic (MASAC). We demonstrate this on two simulated multi-robot tasks, where MAMBPO achieves a similar performance to MASAC, but requires far fewer samples to do so. Through this, we take an important step towards making real-life learning for multi-robot systems possible.
Speaking things into existence: Ontological foundations of identity representation and management

Abstract Conceptual models capture knowledge about domains of reality. Therefore, conceptual models and their modelling constructs should be based on theories about the world—that is, they should be grounded in ontology. Identity is fundamental to ontology and conceptual modelling because it addresses the very existence of objects and conceptual systems in general. Classification involves grouping objects that share similarities and delineating them from objects that fall under other concepts (qualitative identity). However, among objects that fall under the same concept, we must also distinguish between individual objects (individual identity). In this paper, we analyze the ontological question of identity, focusing specifically on institutional identity, which is the identity of socially constructed institutional objects. An institutional entity is a language construct that is ‘spoken into existence’. We elaborate on how institutional identity changes how we understand conceptual modelling and the models produced. We show that different models result if we base modelling on a property‐based conception of identity compared to an institutional one. We use the Bunge‐Wand‐Weber principles, which embrace a property‐based view of identity, as an anchor to the existing literature to point out how this type of ontology sidesteps identity in general and institutional identity in particular. We contribute theoretically by providing the first in‐depth ontological analysis of what the notion of institutional identity can bring to conceptual modelling. We also contribute a solid ontological grounding of identity management and the identity of things in digital infrastructures.
A shape reconstruction and visualization method for a flexible hinged plate using binocular vision

• A detecting system for shape reconstruction of a hinged plate based on binocular vision is constructed. • A shape reconstruction is based on the surface fitting principle. • 3D coordinates are extracted and a shape reconstruction and visualization method is realized by OpenGL. • The amplitude of deformation and the stiffness of flexible hinges are investigated. A binocular vision-based shape deformation reconstruction and visualization method for a flexible hinged plate is proposed. The deformation of the flexible hinged plate in bending mode and torsional mode is investigated. In order to measure the deformation, a binocular vision system is used as the detector. Image processing functions are called to obtain offsets of the mark points pasted on the plate surface. Several types of surface fitting methods are implemented to fit the mark point array into a curved surface. The shape reconstruction graphics of the flexible hinged plate can be obtained. Moreover, the influence of deformation amplitude and stiffness of two flexible hinges on visualization results is studied. The simulation work for curve fitting is conducted by using the measured signals by the binocular stereo vision. The experiments on visual measurement, shape reconstruction and visualization are conducted, using the constructed experimental system and investigated methods. The experimental results show that the quadratic fitting method can be a better curved surface fit for the flexible hinged plate both in bending mode and in torsional mode, and visualization can be realized in real time.
Application of Machine Learning for Online Reputation Systems

Users on the Internet usually require venues to provide better purchasing recommendations. This can be provided by a reputation system that processes ratings to provide recommendations. The rating aggregation process is a main part of reputation systems to produce global opinions about the product quality. Naive methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new ratings. Other sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers’ profile data. This paper proposes a new reputation system using machine learning to predict reliability of consumers from their profile. In particular, we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability, which serve as an input to machine learning algorithms. The predicted weight is then integrated with a weighted average method to compute product reputation score. The proposed model has been evaluated over three MovieLens benchmarking datasets, using 10-folds cross validation. Furthermore, the performance of the proposed model has been compared to previous published rating aggregation models. The obtained results were promising which suggest that the proposed approach could be a potential solution for reputation systems. The results of the comparison demonstrated the accuracy of our models. Finally, the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.
An IoT-based framework for remote fall monitoring

Fall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower elderly's chances of survival, especially if living alone. Hence, the need is there for developing fall detection algorithms with high accuracy. This paper presents a novel IoT-based system for fall detection that includes a sensing device transmitting data to a mobile application through a cloud-connected gateway device. Then, the focus is shifted to the algorithmic aspect where multiple features are extracted from 3-axis accelerometer data taken from existing datasets. The results emphasize on the significance of Continuous Wavelet Transform (CWT) as an influential feature for determining falls. CWT, Signal Energy (SE), Signal Magnitude Area (SMA), and Signal Vector Magnitude (SVM) features have shown promising classification results using K-Nearest Neighbors (KNN) and E-Nearest Neighbors (ENN). For all performance metrics (accuracy, recall, precision, specificity, and F1 score), the achieved results are higher than 95% for a dataset of small size, while more than 98.47% score is achieved in the aforementioned criteria over the UniMiB-SHAR dataset by the same algorithms, where the classification time for a single test record is extremely efficient and is real-time.
Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers’ opinions about products assists to maintain their competitive conditions. We intend to analyze the users’ opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words’ relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F -score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.