Reviewerly/exHarmony huggingface dataset:
Microblog Sentiment Analysis Using User Similarity and Interaction-Based Social Relations

With the rapid development of information technology, microblog sentiment analysis (MSA) has become a popular research topic extensively examined in the literature. Microblogging messages are usually short, unstructured, contain less information, creating a significant challenge for the application of traditional content-based methods. In this study, the authors propose a novel method, MSA-USSR, in which user similarity information and interaction-based social relations information are combined to build sentiment relationships between microblogging data. They make use of these microblog–microblog sentiment relations to train the sentiment polarity classification classifier. Two Sina-Weibo datasets were utilized to verify the proposed model. The experimental results show that the proposed method has a better sentiment classification accuracy and F1-score than the content-based support vector machine (SVM) method and the state-of-the-art supervised model known as SANT.
Finding Frequent Elements for a Product of Partial Orders and Association Rules

A known method for finding frequent sets of attributes in the search for association rules in a binary database is based on constructing an FP-tree (Frequent Pattern Growth Tree). In the case of nonbinary data, the values of each nonbinary attribute are usually binarized using a numerical threshold, and the problem is reduced to constructing the classical FP-tree. The result significantly depends on the choice of thresholds. In this paper, it is proposed to modify the structure of the classical FP-tree by introducing an additional vertex for each nonbinary attribute; this vertex is called full, and it contains information about possible variants of binarization of this attribute values. The proposed model of the FP-tree is called full FP-tree. The application of the full FP-tree for the analysis of nonbinary data under the condition that partial orders on the attribute values are specified. Illustrative and generated examples are discussed.
Analogy-based Assessment of Domain-specific Word Embeddings

The ability of word embeddings to identify shared semantic regularities between word pair categories such as capital-country has led to the use of analogies as a method of validating word embedding models. Further research has shown that relative to the complete breadth of possible analogy categories, there exists a limit to the particular categories accessible, in terms of accuracy, to current analogy equations executed against word embeddings trained on generalized, non domain-specific text corpora. As most, if not all, domain-specific, scientific analogy pairs belong to problematic analogy categories (i.e. the lexicographical and the encyclopedic), we examine the degree to which a domain-specific text corpus and vocabulary positively improve analogy predictions from word embeddings. Our findings demonstrate that in comparison to analogy-based tests performed against general word embeddings, predictions by domain-specific word embeddings outperform in exactly those analogy categories that are both highly problematic and the location of domain knowledge.
Encoding qubits into harmonic-oscillator modes via quantum walks in phase space

We provide a theoretical framework for encoding arbitrary logical states of a quantum bit (qubit) into a continuous-variable quantum mode through quantum walks. Starting with a squeezed-vacuum state of the quantum mode, we show that quantum walks of the state in phase space can generate output states that are variants of codeword states originally put forward by Gottesman, Kitaev, and Preskill (GKP) [Phys. Rev. A {\bf 64}, 012310 (2001)]. In particular, with a coin-toss transformation that projects the quantum coin onto the diagonal coin-state, we show that the resulting {\em dissipative} quantum walks can generate qubit encoding akin to the prototypical GKP encoding. We analyze the performance of these codewords for error corrections and find that even without optimization our codewords outperform the GKP ones by a narrow margin. Using the circuit representation, we provide a general architecture for the implementation of this encoding scheme and discuss its possible realization through circuit quantum-electrodynamics systems.
Cognitive Computing Intelligence to Assist Avionics Analytics

Safety aviation assessment is an increasing concern in Air and Unmanned Aerial System Traffic Management (ATM/UTM). For ATM/UTM situations, there is a need to investigate methodologies and tools to assist air personnel in making decisions. This paper discusses a cognitive model for a Decision-Support System (DSS) to facilitate ATM/UTM. The knowledge-intensive modeling is meant to be the foundation of the comprehension needed for situation awareness about the surroundings of the operation context of the DSS. This paper presents results from dependability verification (logical consistency) of an Avionics Analytics Ontology (AAO). The verification process involves airspace situations including weather conditions and drones. Concluding remarks and further steps for this research work are also discussed.
Investigating Partner Diversification Methods in Cooperative Multi-agent Deep Reinforcement Learning

Overfitting to learning partners is a known problem, in multi-agent reinforcement learning (MARL), due to the co-evolution of learning agents. Previous works explicitly add diversity to learning partners for mitigating this problem. However, since there are many approaches for introducing diversity, it is not clear which one should be used under what circumstances. In this work, we clarify the situation and reveal that widely used methods such as partner sampling and population-based training are unreliable at introducing diversity under fully cooperative multi-agent Markov decision process. We find that generating pre-trained partners is a simple yet effective procedure to achieve diversity. Finally, we highlight the impact of diversified learning partners on the generalization of learning agents using cross-play and ad-hoc team performance as evaluation metrics.
Knowledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph

The obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant. We utilize the numerical information in EMRs and the external knowledge from Chinese Obstetric Knowledge Graph (COKG) to enhance the text representation of EMRs. Specifically, the bidirectional maximum matching method and similarity-based approach are used to obtain the entities set contained in EMRs and linked to the COKG. The final knowledge representation is obtained by a weight-based disease prediction algorithm, and it is fused with the text representation through a linear weighting method. Experiment results show that our approach can bring about +3.53 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.
AKC-Based Revocable ABE Schemes from LWE Assumption

The emergence of quantum computing threatens many classical cryptographic schemes, leading to the innovations in public-key cryptography for postquantum cryptography primitives and protocols that resist to quantum attacks. Lattice-based cryptography is considered to be one of the promising mathematical approaches to achieving security resistant to quantum attacks, which could be built on the learning with errors (LWE) problem and its variants. The fundamental building blocks of protocols for public-key encryption (PKE) and key encapsulation mechanism (KEM) submitted to the National Institute of Standards and Technology (NIST) based on LWE and its variants are called key consensus (KC) and asymmetric key consensus (AKC) by Jin et al. They are powerful tools for constructing PKE schemes. In this work, we further demonstrate the power of KC/AKC by proposing two special types of PKE schemes, namely, revocable attribute-based encryption (RABE). To be specific, on the basis of AKC and PKE/KEM protocols submitted to the NIST based on LWE and its variants, combined with full-rank difference, trapdoor on lattices, sampling algorithms, leftover hash lemma, and binary tree structure, we propose two directly revocable ciphertext-policy attribute-based encryption (DR-ABE) schemes from LWE, which support flexible threshold access policies on multivalued attributes, achieving user-level and attribute-level user revocation, respectively. Specifically, the construction of the ciphertext is derived from AKC, and the revocation list is defined and embedded into the ciphertext by the message sender to revoke a user in the user-level revocable scheme or revoke some attributes of a certain user in the attribute-level revocable scheme. We also discuss how to outsource decryption and reduce the workload for the end user. Our schemes proved to be secure in the standard model, assuming the hardness of the LWE problem. The two schemes imply the versatility of KC/AKC.
Augmented Reality in the Construction Industry: An Industry’s Perspective of Its Users, Phases, and Future Trends

Construction plays an important role in the prosperity of nations and is expected to grow to new heights in the next decade. This significant expansion along with the increased complexity and sophistication of construction projects and rapid technological advances of Industry 4.0 highlights the pressing need for improvement in the construction industry. Consequently, construction companies have turned to innovative techniques to remain competitive and to grow in the modern market. Among the different Industry 4.0 technologies, industrial interest in augmented reality (AR) has increased in the past decade, across sectors including automotive, aerospace, marketing, gaming, and others. Construction has begun to follow suit in this area. Various researchers have explored how AR can be integrated in construction, but no research project has yet comprehensively investigated who benefits the most from AR and where the technology is most useful. Using 128 responses collected from industry practitioners, this paper explores the integration of AR among different construction stakeholders and throughout the entire lifecycle of a construction project. Results showed that architects/engineers and project engineers are perceived to benefit the most from AR and that the technology is expected to be used the most during design, pre-construction planning, construction, and conceptual planning phases. The paper also provides insights into the future adoption of AR by construction stakeholders and by the industry as a whole.
Real-Time Embedded Person Detection and Tracking for Shopping Behaviour Analysis

Shopping behaviour analysis through counting and tracking of people in shop-like environments offers valuable information for store operators and provides key insights in the stores layout (e.g. frequently visited spots). Instead of using extra staff for this, automated on-premise solutions are preferred. These automated systems should be cost-effective, preferably on lightweight embedded hardware, work in very challenging situations (e.g. handling occlusions) and preferably work real-time. We solve this challenge by implementing a real-time TensorRT optimized YOLOv3-based pedestrian detector, on a Jetson TX2 hardware platform. By combining the detector with a sparse optical flow tracker we assign a unique ID to each customer and tackle the problem of loosing partially occluded customers. Our detector-tracker based solution achieves an average precision of 81.59% at a processing speed of 10 FPS. Besides valuable statistics, heat maps of frequently visited spots are extracted and used as an overlay on the video stream.
Annotation practices in Android apps

Understanding the adoption and usage of any programming language feature is crucial for improving it. Existing studies indicate that Java annotations are widely used by developers. However, there is currently no empirical data on annotation usage in Android apps. Android apps are often smaller than general Java applications and typically use Android APIs or specific libraries catered to the mobile environment. Therefore, it is not clear if the results of existing Java studies hold for Android apps. In this paper, we investigate annotation practices in Android apps through an empirical study of 1,141 open-source apps. Using previously studied metrics, we first compare annotation usage in Android apps to existing results from general Java applications. Then, for the first time, we study why developers declare custom annotations. Our results show that the density of annotations and the values of various other annotation metrics are notably less in Android apps than in Java projects. Additionally, the types of annotations used in Android apps are different than those in Java, with many Android-specific annotations. These results imply that researchers may need to distinguish mobile apps while performing studies on programming language features. However, we also found examples of extreme usage of annotations with, for example, a large number of attributes, as well as a low adoption rate for most annotations. By looking at such results, annotation designers can assess adoption patterns and take various improvement measures, such as modularizing their offered annotations or cleaning up unused ones. Finally, we find that developers declare custom annotations in different apps but with the same purpose, which presents an opportunity for annotation designers to create new annotations.
Design and Implementation of a Big Data Evaluator Recommendation System Using Deep Learning Methodology

As the number of researchers in South Korea has grown, there is increasing dissatisfaction with the selection process for national research and development (R&amp;D) projects among unsuccessful applicants. In this study, we designed a system that can recommend the best possible R&amp;D evaluators using big data that are collected from related systems, refined, and analyzed. Our big data recommendation system compares keywords extracted from applications and from the full-text of the achievements of the evaluator candidates. Weights for different keywords are scored using the term frequency–inverse document frequency algorithm. Comparing the keywords extracted from the achievement of the evaluator candidates’, a project comparison module searches, scores, and ranks these achievements similarly to the project applications. The similarity scoring module calculates the overall similarity scores for different candidates based on the project comparison module scores. To assess the performance of the evaluator candidate recommendation system, 61 applications in three Review Board (RB) research fields (system fusion, organic biochemistry, and Korean literature) were recommended as the evaluator candidates by the recommendation system in the same manner as the RB’s recommendation. Our tests reveal that the evaluator candidates recommended by the Korean Review Board and those recommended by our system for 61 applications in different areas, were the same. However, our system performed the recommendation in less time with no bias and fewer personnel. The system requiresrevisions to reflect qualitative indicators, such as journal reputation, before it can entirely replace the current evaluator recommendation process.
Two Modern Aspects of Intangible Assets – Protection on Blockchain and Funding through ICO

Save Ideas Ltd is an Australia-based company and Internet portal for free and instant double protection of intellectual property (ideas of innovators and anyone), one with the Time Certificate stamp and another one based on blockchain technology. Blockchain protection is being upgraded by Initial Coin Offering (ICO) with process of issuing own crypto tokens for the expansion of Save Ideas and at the same time for funding the most promising registered ideas. Process of ICO as the way of financing will be presented in the case of Save Ideas in this chapter.
Searching for Robustness: Loss Learning for Noisy Classification Tasks

We present a "learning to learn" approach for discovering white-box classification loss functions that are robust to label noise in the training data. We parameterise a flexible family of loss functions using Taylor polynomials, and apply evolutionary strategies to search for noise-robust losses in this space. To learn re-usable loss functions that can apply to new tasks, our fitness function scores their performance in aggregate across a range of training datasets and architectures. The resulting white-box loss provides a simple and fast "plug-and-play" module that enables effective label-noise-robust learning in diverse downstream tasks, without requiring a special training procedure or network architecture. The efficacy of our loss is demonstrated on a variety of datasets with both synthetic and real label noise, where we compare favourably to prior work.
A comparative analysis of the STEM education in Chinese primary and secondary schools

The United States is the country of origin, advocacy and development of STEM education. Around 2012, the research on STEM education began to increase gradually in China's education circle. At present, STEM has become a hot word in China's K-12 stage. Due to the different learning environment and development stage of STEM education, there are many problems in STEM education in China, such as lack of necessary top-level design, incoherence of curriculum system, lack of professional teachers and confusion of educational institutions. Through comparative analysis, this paper puts forward constructive suggestions on the implementation of STEM education in China.
Trajectory Anomaly Detection Based on the Mean Distance Deviation

With the development of science and technology and the explosive growth of data, there will be a lot of trajectories every day. However, how to detect the abnormal trajectory from many trajectories has become a hot issue. In order to study trajectory anomaly detection better, we analyze the Sequential conformal anomaly detection in trajectories based on hausdorff distance (SNN-CAD) method, and propose a new measurement method of trajectory distance Improved Moved Euclidean Distance (IMED) instead of Hausdorff distance, which reduces the computational complexity. In addition, we propose a removing-updating strategy to enhance the conformal prediction (CP). Then, we also put forward our Non-conformity measure (NCM), Mean Distance Deviation. It can enlarge the difference between trajectories more effectively, and detect the abnormal trajectory more accurately. Finally, based on the technical measures mentioned above and under the framework of enhanced conformal prediction theory detection, we also build our own detector called Mean Distance Deviation Detector (MDD-ECAD). Using a large number of synthetic trajectory data and real world trajectory data on two detectors, the experimental results show that MDD-ECAD is much better than SNN-CAD in both accuracy and running time.
A Machine Learning Approach for Image Registration Accuracy Estimation

This work continues our previous paper devoted to the application of machine learning to predict the accuracy of intensity-based image registration algorithms. Now we consider the estimation of a more complex geometric transformation involving translation, scaling and rotation. The proposed approach includes the training data creation using the Monte Carlo simulation based on the image formation model typical for image-based navigation. Then, based on the generated data, we calculate seventeen features that we have determined to be relevant for this task. The analysis of the feature importances and different machine learning algorithms performances is carried out. As the source maps, we used real images obtained by the AVIRIS radiometer and taken from Yandex maps. The proposed approach is superior to the separately taken Cramér-Rao bound method or the bootstrap simulation which is not surprising since the use of machine learning allows you to combine different sources (algorithms) using their estimates as features.
Active Learning for IoT Data Prioritization in Edge Nodes Over Wireless Networks

The Internet of Things (IoT) has emerged as a key networking infrastructure that connects a large number of sensors, thereby allowing the collection and processing of large amounts of sensor data efficiently. Edge computing involves the deployment of computing devices close to sensor locations in order to process sensor data and derive useful information quickly and efficiently near the source. This improves the quality of the collected data and reduces the strain on the underlying communication networks. The obtained data can be used for a number of inference tasks using machine learning. The majority of machine learning research efforts assume that the collected data is untainted by various effects in the data collection or transmission process. The data-driven models generated with this assumption and the inferences drawn are frequently unusable in practical scenarios. Predictive maintenance is the process of utilizing sensor data obtained from equipment to determine and predict the optimal time for maintenance of the equipment. Using predictive maintenance, the time and money spent on maintaining equipment can be greatly reduced. In this paper, we perform distributed modeling using edge computing to determine the Remaining Useful Lifetime (RUL) of machines used in a manufacturing plant. We analyze the effects of wireless network degradation on the accuracy of the distributed models and propose active learning algorithms to mitigate these effects, thereby improving the usefulness and robustness of predictive maintenance in realistic settings.
Language representation learning models

Recently, Natural Language Processing has shown significant development, especially in text mining and analysis. An important task in this area is learning vector-space representations of text. Since various machine learning algorithms require representing their inputs in a vector format. In this paper, we highlight the most important language representation learning models used in the literature, ranging from the free contextual approaches like word2vec and Glove until the appearance of recent modern contextualized approaches such as ELMo, BERT, and XLNet. We show and discuss their main architectures and their main strengths and limits.
Audinet: A Decentralized Auditing System for Cloud Storage

In cloud storage, remote data auditing is designed to verify the integrity of cloud data on behalf of cloud users. The audit is performed by a third-party auditor (TPA) according to auditing protocols such as proof of retrievability and provable data possession. However, the TPA-based auditing framework leads to single-point failures, opaque audit processes and undetected mistakes. In this paper, we propose a decentralized auditing system in which the audit is performed by multiple auditors and the audit result is reached in a collaborative and transparent way. Auditors are selected for each audit randomly from the set of cloud users via modified cryptographic sortition; auditing procedures are implemented using a smart contract, and auditing records are published on a blockchain; an incentive mechanism is provided to regulate the behavior of system participants. We implement a prototype system and demonstrate that the proposed system is reliable and technically feasible.