Reviewerly/exHarmony huggingface dataset:
DICE: Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis

The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets.
Devops, A New Approach To Cloud Development &amp; Testing

Organization's proficiency to deliver services and applications at high velocity requires competing effectively in the market. The practices and tools for such management processes demands the quick and reliable model. Changes need to start at the software engineering level when building applications in the cloud, so there is a need to automate our DevOps processes using cloud and non-cloud DevOps automation tools. The aim of this paper is to move DevOps to Cloud and become more agile at software development and operations. In parallel, consideration of how to extend those DevOps process and automation into public and/or private clouds are the prime approach for this research. While investigating the emergence of DevOps, this paper paints it as a dramatic change in IT world for process improvement. Goal is to understand how the DevOps and Cloud work together to help businesses achieve their transformation goals.
Adaptive Density-Based Spatial Clustering for Massive Data Analysis

Clustering is a classical research field due to its broad applications in data mining such as emotion detection, event extraction and topic discovery.It aims to discover intrinsic patterns which can be formed as clusters from a collection of data.Significant progress have been made by the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and its variants.However, there is a major limitation that current density-based algorithms suffer from linear connection problem, where they perform poorly to discriminate objective clusters which are ''connected'' by a few data points.Moreover, the parameter setting and the time cost make it hard to be well-adapted in massive data analysis.To address these problems, we propose a novel adaptive density-based spatial clustering algorithm called Ada-DBSCAN, which consists of a data block splitter and a data block merger, coordinated by local clustering and global clustering.We conduct extensive experiments on both artificial and real-world datasets to evaluate the effectiveness of Ada-DBSCAN.Experimental results show that our algorithm evidently outperforms several strong baselines in both clustering accuracy and human evaluation.Besides, Ada-DBSCAN shows significant improvement of efficiency compared with DBSCAN.
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning

We address one-shot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration. While there has been exciting progress in this direction, most of the approaches still require a few hundred tasks for meta-training, which limits the scalability of the approaches. Our main contribution is to formulate one-shot imitation learning as a symbolic planning problem along with the symbol grounding problem. This formulation disentangles the policy execution from the inter-task generalization and leads to better data efficiency. The key technical challenge is that the symbol grounding is prone to error with limited training data and leads to subsequent symbolic planning failures. We address this challenge by proposing a continuous relaxation of the discrete symbolic planner that directly plans on the probabilistic outputs of the symbol grounding model. Our continuous relaxation of the planner can still leverage the information contained in the probabilistic symbol grounding and significantly improve over the baseline planner for the one-shot imitation learning tasks without using large training data.
Enriched Latent Dirichlet Allocation for Sentiment Analysis

Abstract One of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other parameters. In this study, three novel topic models called date sentiment LDA (DSLDA), author–date sentiment LDA (ADSLDA), and pack–author–date sentiment LDA (PADSLDA) are proposed. The proposed models extend LDA through some extra parameters such as date, author, helpfulness, sentiment, and subtopic. The proposed models use helpfulness in the Gibbs sampling algorithm. Helpfulness is a part of readers who found the review helpful. The proposed models divide the words into two categories: the words more affected by the distribution of subtopic and the words more affected by the main topic. In this study, a new concept called pack is introduced, and a new model called PADSLDA is proposed for sentiment analysis at pack level. The proposed models outperformed the baseline models because according to evaluations results, the extra parameters can appropriately affect the generating process of words in a review. Sentiment analysis at the document level, perplexity, and topic coherence are the main parameters used in the evaluations.
Real-Time Anomaly Detection for Flight Testing Using AutoEncoder and LSTM

Flight testing is crucial in validating the functionality and safety in new commercial aircraft design before mass production. The challenge is to support real-time analysis of high-dimensional time series data generated from tens of thousands of sensors around the aircraft during test flights. We propose a novel 2-stage approach, using a fine-tuned autoencoder to extract the generic underlying features of high-dimensional data, followed by a stacked LSTM using the learned features to predict aircraft time series and to detect anomalies in real-time for flight testing. A novel Timestep(TS)-buffer is introduced to avoid redundant calculations of LSTM gate operations to reduce system latency. Compared with a software implementation of the AutoEncoder-LSTM on CPU and GPU, our FPGA design is respectively 36.3 and 23.9 times faster and consumes 247 and 499 times less energy.
A High-Level Synthesis Approach to the Software/Hardware Codesign of NTT-Based Post-Quantum Cryptography Algorithms

Due to an emerging threat of quantum computing, one of the major challenges facing the cryptographic community is a timely transition from traditional public-key cryptosystems, such as RSA and Elliptic Curve Cryptography, to a new class of algorithms, collectively referred to as Post-Quantum Cryptography (PQC). Several promising candidates for a new PQC standard can have their software and hardware implementations accelerated using the Num-ber Theoretic Transform (NTT). In this paper, we present an improved hardware architecture for NTT, with the hardware-friendly modular reduction, and demonstrate that this architecture can be efficiently implemented in hardware using High-Level Synthesis (HLS). The novel feature of the proposed architecture is an original memory write-back scheme, which assists in preparing coefficients for performing later NTT stages, saving memory storage used for precomputed constants. Our design is the most efficient for the case when log2N is even. The latency of our proposed architecture is approximately equal to (N log2(N) +3N)/4 clock cycles. As a proof of concept, we implemented the NTT operation for several parameter sets used in the PQC algorithms NewHope, FALCON, qTESLA, and CRYSTALS-DILITHIUM.
Learning Actions from Human Demonstration Video for Robotic Manipulation

Learning actions from human demonstration is an emerging trend for designing intelligent robotic systems, which can be referred as video to command. The performance of such approach highly relies on the quality of video captioning. However, the general video captioning methods focus more on the understanding of the full frame, lacking of consideration on the specific object of interests in robotic manipulations. We propose a novel deep model to learn actions from human demonstration video for robotic manipulation. It consists of two deep networks, grasp detection network (GNet) and video captioning network (CNet). GNet performs two functions: providing grasp solutions and extracting the local features for the object of interests in robotic manipulation. CNet outputs the captioning results by fusing the features of both full frames and local objects. Experimental results on UR5 robotic arm show that our method could produce more accurate command from video demonstration than state-of-the-art work, thereby leading to more robust grasping performance.
Analyzing determinants of environmental conduct in small and medium-sized enterprises: A sociotechnical approach

People are increasingly concerned about environmental issues. This concern presents new challenges for companies and impels them to incorporate environmental sustainability into their business strategies. However, smaller companies, such as small and medium-sized enterprises (SMEs), have often been prevented from considering environmentally friendly investments beyond those legally required because these firms lack a holistic perspective on sustainability. They also have a limited perception of the interdependence of corporate, economic, and environmental business components. The long-term survival of SMEs could be significantly strengthened by a holistic understanding based on integrated methods that facilitate the identification and representation of determinants of environmental conduct. These methods would further help to enhance these companies’ competitive advantages. This study sought to use fuzzy cognitive mapping techniques and the system dynamics (SD) approach to carry out analyses of determinants of environmental conduct in SMEs. The results show that this dual methodology enriches the decision-making process as it enables SME managers and decision makers to anticipate and analyze the consequences of their environmental conduct decisions. The results were validated by both the department head and a board member of Portugal’s Regional Proximity and Licensing Department of the Institute for the Support of Small and Medium-sized Enterprises and Innovation (IAPMEI in Portuguese). Static and dynamic analyses were carried out to test and more fully develop the proposed framework.
A new approach for instance selection: Algorithms, evaluation, and comparisons

Several approaches for instance selection have been put forward as a primary step to increase the efficiency and accuracy of algorithms applied to mine big data. The instance selection task scales indeed big data down by removing irrelevant, redundant, and unreliable data, which, in turn, reduces the computational resources necessary for completing the mining task. The local density-based approaches are recently acknowledged as feasible approaches in terms of reduction rate, effectiveness, and computation time metrics. However, these approaches endure low classification accuracy results compared with other approaches. In this manuscript, we propose a new layered and operational approach to address these limitations as well as advance the state-of-the-art by balancing among classification accuracy, reduction rate, and time complexity. We commence by designing a new algorithm (called GDIS) that selects most relevant instances using a global density and relevance functions. This enable us to consider a global view overall a data set to get a better classification accuracy results than current density-based approaches. We design another novel algorithm (called EGDIS), which maintains the effectiveness results of the GDIS algorithm while improving reduction rate results. Moreover, we compare our algorithms against three state-of-the-art algorithms to validate their performance. We develop a Java toolkit called ISTK on the top of the GDIS and EGDIS algorithms, the density-based approaches, and the state-of-the-art algorithms. We also develop a suitable user interface and its management and validation capabilities to ease-of-use and visualize results and data sets. We evaluate and test the performance of our algorithms in terms of four metrics (reduction rate, classification accuracy, effectiveness, and computation time) using twenty-four standard data sets and conduct an intensive set of experiments. The experimental results proved that the GDIS algorithm outperforms the density-based approaches in terms of classification accuracy and effectiveness, the EGDIS algorithm outperforms the density-based approaches in terms of reduction rate and effectiveness, and the GDIS and EGDIS algorithms outperform the state-of-the-art algorithms in terms of achieving a good results in both the effectiveness and computation time metrics. We finally test the scalability and compute experimentally the polynomial-time complexity of our algorithms.
Enhancement of QR Code Capacity by Encrypted Lossless Compression Technology for Verification of Secure E-Document

This paper provides a novel method to improve the data storage of a quick response code (QR code) by applying encrypted lossless compression technology.QR codes are used in several domains, particularly when there is a need to transfer various types of text information.A key aspect of this work is to thus propose a new methodology to overcome the weaknesses of the limited size of the traditional QR code, which has long been an important issue in a wide range of areas.The proposed algorithm incorporates a clear and simple plan for overcoming this difficulty by inserting confidential information into a QR code message.The QR code is updated through the addition of levels that help to share secure messages of various sizes and to authenticate documents for verification and validation.In this work, the newly proposed QR code does not reconstruct the configuration or structure of the QR code.Rather, it provides better security because it relies on the features of the Huffman compression algorithm to reduce the size of the input data and the principles of encryption through the XOR function, which is done through a variable encryption key.The experimental results show the superiority of our method over the previous methods.The scope of this endeavour is thus wide, and there is potential for the encoding of different types of data with a high compression rate in the near future.
What do we know about information security governance?

Purpose This paper aims to review the information security governance (ISG) literature and emphasises the tensions that exist at the intersection of the rapidly changing business climate and the current body of knowledge on ISG. Design/methodology/approach The intention of the authors was to conduct a systematic literature review. However, owing to limited empirical papers in ISG research, this paper is more conceptually organised. Findings This paper shows that security has shifted from a narrow-focused isolated issue towards a strategic business issue with “from the basement to the boardroom” implications. The key takeaway is that protecting the organisation is important, but organizations must also develop strategies to ensure resilient businesses to take advantage of the opportunities that digitalization can bring. Research limitations/implications The concept of DSG is a new research territory that addresses the limitations and gaps of traditional ISG approaches in a digital context. To this extent, organisational theories are suggested to help build knowledge that offers a deeper understanding than that provided by the too often used practical approaches in ISG research. Practical implications This paper supports practitioners and decision makers by providing a deeper understanding of how organisations and their security approaches are actually affected by digitalisation. Social implications This paper helps individuals to understand that they have increasing rights with regard to privacy and security and a say in what parties they assign business to. Originality/value This paper makes a novel contribution to ISG research. To the authors’ knowledge, this is the first attempt to review and structure the ISG literature.
SHAREChain: Healthcare data sharing framework using Blockchain-registry and FHIR

The amount of healthcare data is increasing. However, issues with unstandardized data formats and the reliability of provenance make the data challenging to share with other institutions. This paper proposes a standards-based sharing framework, SHAREChain, which incorporates two features to deal with reliability and interoperability issues. First, it improves reliability by using the data integrity of a Blockchain-registry and constitutes a Consortium Blockchain Network to share data solely between authenticated institutions. The second feature improves interoperability with standards relating to healthcare data sharing: Fast Healthcare Interoperability Resources and Cross-Enterprise Document Sharing.
Enhancing STEM competence by making electronic musical pencil for non-engineering students

STEM education has received increasing attention in recent years. Researchers have indicated that STEM learning activities should be closely connected to real-world problems to facilitate students' learning and foster positive STEM attitudes. In response to this recommendation, this study aims to investigate the effectiveness of non-engineering students' making electronic musical pencil on their STEM competence. An instructional design based on knowledge, skills, and attitudes (KSA) enhancement framework was proposed. Data were collected from 22 non-engineering students, the experimental results show that the effect of making electronic musical pencil by adopting the KSA enhancement framework on non-engineering students not only produces positive STEM competence learning outcomes according to Bloom's taxonomy but also enhances learners' attitude changes towards STEM.
A subtree‐based transformation model for cryptosystem using chaotic maps under cloud computing environment for fuzzy user data sharing

Summary Cloud computing technologies have been prospering in recent years and have opened an avenue for a wide variety of forms of adaptable data sharing. Taking advantage of these state‐of‐the‐art innovations, the cloud storage data owner must, however, use a suitable identity‐based cryptographic mechanism to ensure the safety prerequisites while sharing data to large numbers of cloud data users with fuzzy identities. As a successful way to guarantee secure fuzzy sharing of cloud data, the identity‐based cryptographic technology still faces an effectiveness problem under multireceiver configurations. The chaos theory is considered a reasonable strategy for reducing computational complexity while meeting the cryptographic protocol's security needs. In an identity‐based cryptographic protocol, public keys for individual clients are distributed, allowing the clients to separately select their own network identities or names as their public keys. In fact, in a public‐key cryptographic protocol, it is for the best that the confirmation of the public key is done in a safe, private manner, because this way the load of storage on the server's side can be considerably relieved. The objective of this paper is to outline and examine a conversion process that can transfer cryptosystems using Chebyshev's chaotic maps over the Galois field to a subtree‐based protocol in the cloud computing setting for fuzzy user data sharing, as opposed to reconcocting a different structure. Furthermore, in the design of our conversion process, no adjustment of the original cryptosystem based on chaotic maps is needed.
Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions

The increasing penetration of distributed renewable energy sources like Photovoltaics (PV) may form a threat to reliable grid operation. PV-systems impede load balancing due to the intermittent and uncontrollable power production. The development of highly accurate forecasting techniques is essential to support a high PV penetration rate in the local electricity grid. This research examines the performance of different machine learning (ML) models that autonomously predict day-ahead power production of individual and aggregated PV-systems. The forecasting models are developed by considering historic power production and regional predictions of weather metrics. The method allows to generate site specific forecasting algorithms that inherently account for site characteristics including size, orientation and shading and is independent of such input. In the research we evaluate the accuracy of the forecasting models for 152 PV-systems individually and for different levels of aggregated systems. With a skill score of respectively 41.4% and 41.3% Gradient Boosting and Random Forests are found to outperform the other models. This is closely followed by the Feed-forward Neural Network and Kernel Support Vector Machine models. Moreover, the results show the value of aggregating PV sites in day-ahead power forecasting as the mean absolute error and the root mean square error of each ML model improve by at least 18% and 20%.
Cybersecurity investments in a two-echelon supply chain with third-party risk propagation

Cybersecurity presents a monumental challenge for interconnected supply chains, as an attack on one node can compromise an entire business. In this paper, we propose a game theory model to investigate cybersecurity investments with third-party risk propagation in a two-echelon supply chain consisting of one retailer and n suppliers. The optimal investments and their responses to relevant security characteristics, such as intrinsic vulnerability, propagation probability, number of suppliers, and attack probability, are analysed and discussed both theoretically and numerically considering one-stage risk propagation. It is found that there are serious prisoners' dilemma and free-riding phenomena in such a scenario. To mitigate third-party risks and improve the investment efficiency, three coordination mechanisms, joint decision, security risk compensation, and security information sharing, are presented and compared numerically. The results indicate that joint decision-making and security risk compensation perform better on stimulating firms' investments and reducing expected costs both individually and collectively relative to security information sharing. Furthermore, the case of two-stage risk propagation is also supplemented and compared with one-stage case. Based on these findings, some management insights are recommended to cybersecurity managers in supply chains for designing more efficient cybersecurity mechanisms and investment strategies.